Conflict-free scheduling: Ensures no two vehicles with intersecting paths enter at the same time.
Academic Writing, Data Science, My Digital Universe, Portfolio

Optimizing Traffic Flow: Efficient, but Is It Safe?

Cheran Ratnam

Unsignalized intersections are managed without traffic lights. They rely on stop signs and right-of-way rules. These intersections are inherently riskier compared to traffic light enforced ones because now there is no lights and it depends on the driver paying attention to the stop sign, but that is a different matter altogether.

They’re common in suburban or low-traffic areas but increasingly challenged by growing traffic volumes and the emergence of Connected and Automated Vehicles (CAVs).

These intersections are friction points in modern traffic systems. And the problem often starts with one outdated rule: First-Come-First-Served (FCFS).

First-Come-First-Served

FCFS is a simple scheduling principle: vehicles cross in the order they arrive. If multiple vehicles approach, each waits for the ones ahead (yes, you are supposed to wait if the other person arrives at the stop sign before you) even if their paths don’t conflict.

Why It Falls Short

  • No spatial awareness: Vehicles wait even when their paths don’t intersect. This may not be a bad thing if your city or neighborhood has CRAZY drivers but it is not efficient right?
  • Ignores vehicle dynamics: No speed adjustments are used to reduce waiting time. Although you may be able to reply to a text or two? NO. Don’t text and drive!
  • Creates bottlenecks: Delays increase when vehicles arrive from different directions in quick succession. Oh well, your precious time.

In the animation above, each vehicle waits for the previous one to clear the intersection, even when there’s no collision risk. The result? Wasted time and unused intersection space. Well, that is if you only care about efficiency. Not so bad from a safety point of view.

Why FCFS Doesn’t Work for CAVs

As vehicles become more intelligent and connected, relying on a static rule like FCFS is inefficient. This is assuming that the person behind the wheel is also intelligent enough to practice caution and obey traffic rules and drives SOBER.

Modern CAVs can:

  • Share real-time location and speed data.
  • Coordinate with one another to avoid collisions.
  • Adjust their behavior dynamically.

FCFS fails to take advantage of these capabilities. It often causes unnecessary queuing, increasing delays even when safe, efficient crossings are possible through minor speed changes. Again, assuming that the drivers are all outstanding citizens with common sense, yes, this is not very efficient and there is room for improvement.

A Smarter Alternative: Conflict-Free, Real-Time Scheduling

This recent paper, named “An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection” proposes a linear programming-based model to optimize flow at unsignalized intersections. The model is built for CAVs and focuses on minimizing average delay by scheduling optimal crossing times based on:

  • Vehicle location and direction
  • Potential conflict zones

Key Features of the Model

  • Conflict-free scheduling: Ensures no two vehicles with intersecting paths enter at the same time.
  • Rolling horizon optimization: Continuously updates schedules in real time.
  • Delay minimization: Vehicles adjust speed slightly instead of stopping.

In this visualization, vehicles coordinate seamlessly:

  • The red car enters first.
  • The gray car slows slightly to avoid a conflict.
  • The blue car times its approach to maintain flow.

No stopping. No wasted time. Just optimized motion.

Now that all sounds good to me. It sounds somewhat like a California Stop, if you know what I mean. But how can we trust the human to obey these more intricate optimization suggestions when people don’t even adhere to more simple rules like slowing down in a school zone? Ok, maybe a different topic. So let’s assume that these are all goody goodies behind the wheel and continue.

Performance: How the Model Compares to FCFS

According to the study’s simulations:

  • Up to 76.22% reduction in average vehicle delay compared to FCFS.
  • Real-time responsiveness using rolling optimization.
  • Faster computation than standard solvers like Gurobi, making it viable for live deployment.

The result? Smoother traffic, shorter waits, and better use of intersection capacity without traffic signals.

Rethinking the Rules of the Road

FCFS is simple but simplicity comes at a cost. In a connected, data-driven traffic ecosystem, rule-based systems like FCFS are no longer sufficient.

This study makes the case clear: real-time, model-based scheduling is the future of unsignalized intersection management. As cities move toward CAVs and smarter infrastructure, the ability to optimize traffic flow will become not just beneficial, but essential. That said, complexity also comes at a cost. If all the vehicles are autonomous and are controlled by a safe, optimized, and centralized algorithmic command center, this could work. But as soon as you introduce free agency, which is not a bad thing, but in this context it introduces a lot of risk, randomness, uncertainty, and CHAOS … one have to think about efficiency vs. practicality and safety.

If these CAVs are able to enter into a semi-controlled environment when they enter the parameter of the intersection, perhaps this approach could work. This means that while they are in the grid (defined by a region that leads up to the stop sign), the driver does loose some autonomy and their vehicle will be simulated by a central command … this might be a good solution to implement.

Either way, this is an interesting study. After all, we all want to get from point A to point B in the most efficient way possible. The less time we spend behind the wheel at stop signs, the more time we have for … hopefully not scrolling Tik Tok. But hey, even that is better than just sitting at a stop sign, right?

Bar chart showing ChatGPT and BERT agreement with researcher sentiment labels for positive, negative, and neutral categories
Academic Writing, Data Science, My Digital Universe, Portfolio

How Good is Sentiment Analysis? Even Humans Don’t Always Agree

Cheran Ratnam

Sentiment Analysis Is Harder Than It Looks

Sentiment analysis is everywhere: from analyzing customer reviews to tracking political trends. But how accurate is it really? More importantly, how well can machines capture human emotion and nuance in text?

To answer that, I conducted a real-world comparison using a dataset of healthcare-related social media posts. I evaluated the performance of two AI models : ChatGPT and a BERT model fine-tuned on Reddit and Twitter data against human-coded sentiment labels. I used the fine-tuned BERT model and the dataset as part of my Ph.D dissertation. So this was already available to me. More information on the BERT model can be found in my disseration.

The results tell an interesting story about the strengths and limitations of sentiment analysis; not just for machines, but for humans too.

ChatGPT vs. BERT: Which One Came Closer to Human Labels?

Overall, ChatGPT performed better than the BERT model:

  • ChatGPT reached 59.83% agreement with human-coded sentiments on preprocessed text (meaning I used the dataset I used for the BERT model)
  • On raw text, agreement was 58.52% (here, I used the same dataset I provided the second human coder, without any preprocessing like lemmatization, etc)
  • The BERT model lagged behind in both scenarios

This shows that large language models like ChatGPT are moving sentiment analysis closer to human-level understanding; but the gains are nuanced. See below image that shows ChatGPT vs the Trained BERT agreement levels with my coding for each Reddit post. Note that this was when I used the preprocessed dataset and generated the ChatGPT output.

Class-by-Class Comparison: Where Each Model Excels

Looking beyond overall scores, I broke down agreement across each sentiment class. Here’s what I found:

  • Neutral Sentiment: ChatGPT led with 64.76% agreement, outperforming BERT’s 44.76%.
  • Positive Sentiment: BERT did better with 66.19% vs. ChatGPT’s 41.73%.
  • Negative Sentiment: Both struggled, with BERT at 26.09% and ChatGPT at 17.39%.

These results suggest that ChatGPT handles nuance and neutrality better, while BERT tends to over-assign positivity; a common pattern in models trained on social platforms like Reddit and Twitter.

But Wait … Even Humans Don’t Fully Agree

Here’s where it gets more interesting! When comparing two human coders on the same dataset, agreement was just 72.79% overall. Class-specific agreement levels were:

  • Neutral: 91.8%
  • Negative: 60%
  • Positive: Only 43.6%

This mirrors the model behavior. The subjectivity of sentiment, especially when it comes to borderline cases or ambiguous language, is challenging even humans!

Why Sentiment Is So Difficult … Even for Humans

As discussed in my dissertation, sentiment classification is impacted by:

  • Ambiguous or mixed emotions in a single post
  • Sarcasm and figurative language
  • Short posts with little context
  • Different human interpretations of tone and intent

In short: Sentiment is not just about word choice, it’s about context, subtlety, and perception. I tackle this in much more depth in my dissertation. So, if you want to read more about what other researchers are saying, I suggest you refer to Chapter 5, where I talk about sentiment analysis issues, explanations, and implications.

Here is the Spiel

  • ChatGPT outperformed a Reddit-Twitter trained BERT model in both overall accuracy and especially on neutral sentiment.
  • Positive and negative sentiment remain harder to classify, for both models and humans.
  • Even human coders don’t always agree, proving that sentiment is a subjective task by nature.
  • For applications in healthcare, finance, or policy; where precision matters, sentiment analysis needs to be interpreted carefully, not blindly trusted.

Final Thoughts

AI is getting better at understanding us, but it still has blind spots. As we continue to apply sentiment analysis in real-world domains, we must account for ambiguity, human disagreement, and context. More importantly, we need to acknowledge that even “ground truth” isn’t always absolute.

Let’s keep pushing the boundaries, but with a healthy respect for the complexity of human emotion.

"Comparison of traditional time series models like ARIMA with foundation models like TimesFM, SigLLM, and GPT-based anomaly detection approaches"
Academic Writing, Data Science, My Digital Universe, Portfolio

Time Series + LLMs: Hype or Breakthrough?

Cheran Ratnam

Time series foundational models like UniTS and TimesFM are trained on massive, diverse datasets and show promising results in anomaly detection. Surprisingly, even general-purpose LLMs (like GPT) can detect anomalies effectively; without any domain-specific pretraining.

But here’s the reality check:

🔹 LLMs are not always superior to traditional models like ARIMA. In fact, classical statistical models still outperform them in some cases—especially when data is clean and patterns are well-understood.

🔹 Pretrained pipelines like Orion reduce the cost of training from scratch, enabling faster deployment. However, real-time efficiency remains a challenge.

🔹 SigLLM, which converts time series into text for LLM input, is innovative—but rolling window representations make it computationally expensive.

🔹 Despite limitations like context window size and slow inference, LLMs are still flexible enough to be competitive. But they don’t consistently outperform classical models across the board.

👉 The bottom line: LLMs are not a silver bullet. The most effective strategy is often hybrid, combining classical statistical techniques with foundation model strengths.

Are LLMs the future of time series modeling—or just another wave of AI hype?

Let’s discuss.

#AI #TimeSeries #AnomalyDetection #LLMs #FoundationModels
📄 Thesis by Linh K. Nguyen (MIT)

Animated comparison of regression models showing bias, variance, and prediction accuracy
Academic Writing, Data Science, My Digital Universe, Portfolio

🎢 Bias, Variance & the Great Regressor Showdown

Cheran Ratnam

Imagine throwing five regressors into the same ring, giving them the same dataset, and watching them wrestle with reality. That’s what this animation is all about: a visual deep dive into bias, variance, and model complexity—without the textbook-level headache.

The models in play

Five well-known regression models, one smooth sinusoidal target function, and a bunch of noisy data points:

  • Linear Regression – The straight-line enthusiast.
  • Decision Tree – Thinks in boxes, and sometimes forgets how to generalize.
  • Random Forest – The chill ensemble kid who smooths out the chaos.
  • XGBoost – The overachiever with a calculator and an ego.
  • KNN – Your nosy neighbor who always asks, “What are your closest friends doing?”

🎥 The Animation:

The Concepts in Play

🎯 Bias

Bias refers to the error introduced when a model makes overly simplistic assumptions about the data. In other words, it is what happens when the model is too rigid or inflexible to capture the true patterns.

Take Linear Regression for example:

“Let’s pretend everything is a straight line.”

That assumption may work in some cases, but when the data contains curves or more complex relationships, the model cannot adapt. This leads to underfitting, where the model performs poorly on both training and test data because it has failed to learn the underlying structure.

🎢 Variance

Variance measures how sensitive a model is to fluctuations or noise in the training data. A high variance model learns the data too well, including all the random quirks and outliers, which means it performs well on the training set but poorly on new data.

This is typical of models like Decision Trees and KNN:

“I will memorize your quirks and your noise.”

These models often produce excellent training scores but fall apart during testing. That gap in performance is a red flag for overfitting, where the model has essentially memorized instead of generalized.

🤹 Model Complexity

Model complexity describes how flexible a model is in fitting the data. A more complex model can capture intricate patterns and relationships, but that flexibility comes at a cost.

More complexity often means the model has a higher risk of chasing noise rather than signal. It may give impressive training performance but fail when deployed in the real world. Complex models also tend to be harder to interpret and require more data to train effectively.

So while complexity sounds appealing, it is not always the answer. Sometimes the simplest model, with fewer moving parts, delivers the most reliable results.

💡 What We Learn from the GIF

  • Linear Regression has high bias. It’s smooth but can’t capture curves.
  • Decision Tree slices the data too rigidly. Prone to overfitting.
  • Random Forest balances bias and variance quite well (💪).
  • XGBoost tries to win—but often needs careful tuning to avoid bias.
  • KNN loves to follow the data closely, sometimes too closely.

Why This Matters (a lot)

In the real world:

  • Underfitting leads to useless predictions.
  • Overfitting leads to confident but wrong predictions.
  • Balanced models win in production.

Understanding the bias-variance tradeoff helps you:

✅ Pick the right model
✅ Avoid overcomplicating
✅ Diagnose errors
✅ Not trust every “98% R² score” you see on LinkedIn

Final Thoughts

Model performance isn’t magic—it’s tradeoffs. Sometimes the simplest model wins because the data is solid. Other times, the fanciest algorithm trips on its own complexity.

📩 Which model do you think performed best here?
Hit me up with your thoughts or overfitting horror stories.

#MachineLearning #BiasVariance #RegressionModels #ModelComplexity #XGBoost #RandomForest #KNN #Overfitting #Underfitting #DataScience

Comparing Regression Models: Ames vs California Housing Dataset Performance
Data Science, My Digital Universe

Model performance isn’t just about complexity; it’s about context.

Cheran Ratnam

So, I ran a little experiment—because what’s life without overfitting just for fun? I compared five regression models on two very different housing datasets …

🏘️ Ames Housing:

Rich, detailed, and multi-dimensional. Think of it like the Swiss Army knife of regression datasets.

🌴 California Housing:

Simplified down to a single feature — Median Income. Basically, the minimalist’s dream.

Models Compared:

  • Linear Regression
  • Decision Tree
  • Random Forest
  • XGBoost
  • K-Nearest Neighbors (KNN)

Each GIF below shows how performance evolved over time. We’re talking train vs. test R² scores, visible over iterations—plus those visual cues you love that scream “Hey, this one’s overfitting!” or “Yeah… this one’s basically guessing.”

Ames Housing (Multi-Feature)

➡️ Insights:

  • Random Forest flexed its muscles here, but KNN and XGBoost? Classic cases of either overfitting or just not showing up to work.
  • Linear Regression held its own—shocking, I know—thanks to the strength of the underlying features.

California Housing (1 Feature: Median Income)

➡️ Insights:

  • When you only have one strong feature, even simple models like Linear Regression can outperform fancier methods.
  • Most complex models struggled with generalization. XGBoost in particular had a rough day.

💡 Takeaways:

✔️ Model choice matters; but data quality and feature strength matter more
✔️ Overfitting is real. Watch those training R² scores spike while test performance nosedives
✔️ KNN is great… if you enjoy chaos
✔️ Don’t blindly trust complexity to save the day

So, which model would you bet on in each case?

📩 Drop your thoughts in the comments or connect with me on LinkedIn. Always down to talk shop (or rant about why XGBoost occasionally betrays us).

#MachineLearning #ModelEvaluation #DataScience #RegressionModels #XGBoost #RandomForest #ModelOverfitting #HousingData #AmesHousing #CaliforniaHousing #KNN #LinearRegression

Streamlit logistics dashboard with pricing estimator and routing tool
Data Science, My Digital Universe, Portfolio

The Ultimate Logistics Dashboard: Pricing, Routing, and Freight Insights All in One

Cheran Ratnam

Navigating the labyrinth of logistics can often feel like assembling IKEA furniture without the manual—frustrating, time-consuming, and occasionally resulting in a piece that looks nothing like the picture. After wrestling with geography, constraints, and some very opinionated algorithms, I built a dashboard that now supports multi-stop route optimization—up to well many destinations as you want … well, if you are patient (because pushing it to 6 might melt the Streamlit servers… ask me how I know).

Why You’ll Love It:

  • Multi-Stop Planning: Handle up to as many destinations in one go. It’s like having a personal assistant who doesn’t require coffee breaks.​
  • Fuel Stop Integration: Automatically adds fuel stops when needed, so your trucks won’t run on fumes. Because pushing a semi-truck to the next station isn’t a workout anyone wants.​
  • Efficiency at Its Core: Optimizes for fuel efficiency, distance, and delivery sequence, ensuring your routes are as lean as a marathon runner on a kale diet.​

Under the Hood:

Powered by a constrained optimization model using Gurobi, our dashboard calculates the most cost-effective routes by considering distance, fuel costs, and truck load limits. It’s like having Einstein as your co-pilot, minus the wild hair.​

Let’s face it—logistics planning is often less “fast and furious” and more “slow and suspicious.” But fear not, fellow freight folks. The Route Optimizer Dashboard is here to inject a dose of caffeine into your routing workflow (no judgment if that’s your third cup today).

The Dashy Dashy Dashboard 🙂

This dashboard isn’t just a pretty face. It’s divided into three slick tabs:

  • 📦 Pricing Estimator – Give it your shipment details, and it’ll throw back a fee estimate faster than your intern can Google “freight cost per mile.”
  • 🚚 Route Optimizer – Plug in your origin and your destinations, and it’ll find the most fuel-efficient route. It even adds fuel stops when needed. Because running out of gas mid-delivery is not a vibe.
  • 📈 Dashboard – Visualize freight flows, trends, and performance. Yes, we made charts sexy again.

🛠️ How It Works

Input Your Destinations:
Enter as many stops as your freight-loving heart desires (and breathe). But let’s keep expectations real—it’s hosted on free Streamlit Cloud, so if you enter half a dozen points, give it 5-10 seconds to do its thing. Perfect time to sip your coffee and contemplate the mysteries of route efficiency.

Review the Suggested Route:
The backend optimizer (powered by Gurobi) will crunch fuel costs, distances, and stop sequences like a logistics nerd on Red Bull. The output? A smart route ready for action.

Hit the Road:
With your optimized plan in hand, your drivers can focus on the journey—not toggling between Google Maps and guesswork. By the way, I used the alternative fuel stations dataset for this so don’t be alarmed if sometimes the fuel station suggestion is missing or have some funky name … most likely those funky stations are electric. The data is real but I had to work with what was available.

✨Final Thoughts

In the world of logistics, time is money, and efficiency is the name of the game. Our Route Optimizer Dashboard ensures you’re not just playing—you’re winning.​

This dashboard is for the data-minded logistics folks who want to stop reacting and start optimizing. Whether you’re pricing a load, planning a route, or just want to admire some interactive charts, it’s all there.

Try it out here: logistics-kk34nzr4hekiwm2tpxhrmx.streamlit.app

And hey, if it saves you even one angry client call about late deliveries, I’ll consider my job done. Need help integrating something similar for your operation? Let’s chat. This stuff’s kinda my thing.
✉️ Connect with me on LinkedIn

#Logistics #RouteOptimization #Efficiency #FuelSavings #FreightTech #SupplyChain #SmartRouting

Academic Writing, My Digital Universe, Portfolio

Entertainment Overcoming Resistance Model (EORM)

Cheran Ratnam

Entertainment Education programs have been found to create awareness and behavioral changes on social and health issues. The traditional definition of E-E refers to entertainment programs that are designed to convey known, prosocial effects to viewers. The wide range of definitions that describe E-E can be attributed to the variety of goals in E-E programming. Some programs are focused on informing viewers whereas others are focused on changing attitudes or behaviors.

The Extended Elaboration Likelihood Model highlights E-E’s role of resistance and narrative engagement. The Entertainment Overcoming Resistance Model was thus introduced as a way to consider how E-E programs can overcome resistance. EORM states that transportation, identification, similarity, and PSI help overcome various types of resistance to persuasion—enhancing persuasive outcomes.

The article examines how narrative transportation and character involvement reduce three forms of resistance: reactance, counter arguing, and perceived vulnerability. It compares effects between a dramatic-narrative and nonnarrative program that highlighted consequences of unplanned teen pregnancies. It revealed that the dramatic narrative reduced reactance, fostered parasocial interactions, and decreased perceptions of persuasive intent. Identification with characters in the narrative was found to reduce counterarguing— increasing perceived vulnerabilities to unplanned pregnancies. Transportation into the dramatic narrative though, was associated with counterarguing, contrary to the expectations.

Furthermore, noticing a hidden agenda to promote healthy behavior, disguised as entertainment was found to arouse reactance, while a more straightforward attempt to persuade, did not. This is an interesting find and I really liked the suggestions to address such behavior. The authors suggest that E-E creators should begin by first understanding the forms of resistance that operate within their targeted audience, since there are different types of resistance. It is suggested that perceived persuasive intent to be kept lower for audiences with high reactance and to use characters that users can bond with. Another suggestion is to focus on production features that facilitate empathy when the goal is to increase perceived vulnerability, rather than developing similar characters. These suggestions can be very helpful for E-E creators that want to communicate social issues. Being too pushy or being too cunning could turn the audience off to what you are trying to say, especially if it is a controversial topic for the viewer to begin with. So, using a charismatic character as a vehicle to carry the message seems to be a logical approach to me. The suggestion to focus less on the character but more on production features, when the goal is to increase perceived vulnerability, is a bit harder for me to grasp. Perhaps the idea is that in order to increase awareness a mere character would not suffice.

Koops van ’t Jagt, R., Hoeks, J. C. J., Duizer … (2017) conducted research to find out how a Spanish fotonovela (similar to a comic book), about diabetes communicated E-E messaging to high and low proficiency Dutch readers. The results found that when it comes to diabetes knowledge, readers of the fotonovela outperformed participants that were given a traditional brochure. This was true for both high and low proficiency readers. However, they did not score significantly higher than traditional brochure readers when it came to behavioral intentions. The researchers state that perhaps the lack of focus on measuring behavioral intentions could be a reason for the lack of visibility into behavioral significance and that in similar studies behavioral intention too had been found to be significant from E-E exposure.

I think EORM could be applied for advertising efforts, especially when advertising healthcare related products. However, if the advertisement is seen as too cunning or crafty, audiences might display reactance. So, advertising professionals would have to focus a lot on characters as well production features to evoke empathy. In practicality, this approach would be quite difficult to achieve in short 30 second – 1-minute advertisements. However, if advertising efforts were to focus on longer-formats, leveraging EORM could benefit both advertisers as well as audiences, especially when it comes to imparting knowledge.

My question to you is: would you be interested in product marketing efforts that utilize the EORM model? Where would you think such ads would be appropriate? For example, would you be receptive if an EORM ad was playing on your Facebook feed, interrupting something you were watching, or would you rather prefer that it shows up in search results when you are searching for a potential health concern? So, for example, you suspect you might have arthritis and you are trying to find more information on YouTube and happen to come across an E-E video series on YouTube about a character that suffers from similar health issues, would you prefer that piece over something that is served on your Facebook feed?

Citation

Koops van ’t Jagt, R., Hoeks, J. C. J., Duizer, E., Baron, M., Molina, G. B., Unger, J. B., & Jansen, C. J. M. (2017). Sweet Temptations: How Does Reading a Fotonovela About Diabetes Affect Dutch Adults with Different Levels of Literacy? Health Communication, 33(3), 284–290. https://doi.org/10.1080/10410236.2016.1258617

Academic Writing, Portfolio

Cultivation Theory

Cheran Ratnam

George Gerbner first introduced the cultivation theory as a macrolevel system of explanation—an alternative to the micro-focused media research of his time. His approach consisted of three components: the media institutions, mass-produced messages, and the effects they cultivated on people. When first introduced, the theory focused, on a macrolevel, on broadscale institutional practices, widespread meanings, and long-term adoption. In essence, it focused on how mass-produced media messages from media institutions, when widely disseminated, shape public knowledge and belief in the long run.

Gerbner’s team was interested in primetime and children’s weekend morning programming as they analyzed entertainment programming for three commercial TV networks. They considered commercial TV as presenting a “total world of interrelated stories (both drama and news) produced to the same set of market specifications.” They also justified their choices stating that those programs had the most extensive viewership. So, in their view, any widespread meanings across the media landscape would be evident in such a sample. However, over time, researchers started to modify or even move away from certain aspects of Gerbner’s theory. The move towards a micro-focused approach is perhaps one of the most noticeable shifts. Another critical change in usage was the shift in focus from the importance of the media’s dominant meanings to constructing cultivation indicators and then testing which indicators closely relate to heavy TV viewing.

“Watching the rich and famous: the cultivation effect of reality television shows and the mediating role of parasocial experiences” by Jahng, M. R (2019), explores the relationship between reality TV exposure and college students’ beliefs on wealth and materialism. The research found that heavy viewers of reality TV consider wealth (as presented on the show) to be more prevalent and attainable than light viewers. The study also revealed that heavy reality TV viewers hold stronger materialistic beliefs than light viewers, supporting the cultivation theory’s genre-specific effects. The study and a growing number of researchers suggest that cultivation effects should be applied to specific genres rather than applying them broadly across television. The article also examines the impact of media migration by studying viewers’ interactions with reality show casts via social media. I found the article to be extremely engaging and interesting. I have never watched a full episode of reality TV. So, it was interesting to understand how reality TV influences the perceptions of heavy viewers. I also found the cultivation theory’s application to be noteworthy as it deviated from the original widespread media institution approach that Gerbner had proposed.

Social media is not just a secondary media consumption medium anymore. For some, it is the primary medium for news and entertainment—perhaps even replacing TV! Twitter allows users to consume unfiltered content from many sources and provides a hyper-focused experience to users who customize their feeds to keep up with their topics of interest. To study social media usage and its effects on people, the cultivation theory could be applied— exploring the relationship between heavy Twitter use and their beliefs on police brutality, for example. This could give journalists and media professionals more insight into how to rethink their TV content to narrow the polarization of information between audiences.

Social media advertising professionals can narrow their targeting because of the highly detailed personal information collected from social media platforms. Understanding public perceptions on social media privacy and being sensitive to those views is perhaps the best way forward for advertisers—as the ability to track and analyze a user across platforms and connected devices will only increase as more aspects of life become digitized. Most social media users are aware of cookies, tracking, and retargeting. Perhaps understanding how media messages about social media privacy affect user behaviors and perceptions could help social media platforms and advertising professionals to create better explanations of how user data is collected and used.  Macro and microanalyses of the cultivation theory can help advertising professionals garner how social media users feel about using their personal data.

Do you think heavy Twitter users care more and support the need for social justice movements such as BLM, compared to heavy Facebook users? I could rephrase this question by substituting Facebook users with heavy TV users, but I am more interested in users that have adopted new media over traditional media sources.

Citation:

Jahng, M. R. (2019). Watching the rich and famous: the cultivation effect of reality television shows and the mediating role of parasocial experiences. Media Practice & Education, 20(4), 319–333. https://doi-org.libproxy.library.unt.edu/10.1080/25741136.2018.1556544

Poems

The wait

Cheran Ratnam

Waiting for a sunrise,
Chores, all done.
It takes time,
But thoughts don’t stop.

Mountains climbed,
To pass that time,
Hope is all that’s left,
For dreams to hang on.

Just look around,
Some just have it easy.
Even when it’s tough,
They all have something.

Then the mind ponders,
On what eyes have seen,
Gently whispers that;
Time hasn’t been forgiving.

Some nights it’s a wrestle,
Shut-eye seems impossible.
Racing mind, fighting thoughts,
Calming breath, holding down.

Faith says: it shall pass,
Hope says: I’m all you’ve got.
Fear is the shame I’ve got,
What life, is reality, such?

Poems

Over the moon

Cheran Ratnam

I wrote you poems,
Thought you’d notice.
I showed up,
Thought you’d speak.

I sent you thoughts,
Hoping you’ll catch them.
Then I got over you —
Just like that!
Nothing drastic,
No mind altering tactics.

I’m not a waiter.
My heart’s not a restaurant.
I waited long enough.

There’s only one moon,
In this galaxy, that is.
I’m not a fighter,
I’m only a writer.
There are other galaxies,
Different possibilities.
I let my mind wander,
To the distant galaxies yonder,
Maybe they too have bumble and tinder?
New moons are new, when found.
But they existed, all bright and round.

My Digital Universe, Poems, Portfolio

Beyond The Mirage

Cheran Ratnam

Love beyond compare,
Where the lilies bloom and birds nest,
Liberated stars lay in adoration.
Every moment has en eternity,
Encapsulated with majestic splendor.

For the eyes to see and the ears to hear,
For sorrow has long passed,
Left behind the remnants of a life of bewilderment.

Distance shores call me now,
Yet the mind is still, to ponder.
Nothing to add or subtract,
Yet time passes by with this addition and subtraction.

The greater the mountain, nicer the view,
Yet, to get there one needs strength and valor,
Good friends, courage and a bit of hope.
Once conquered, spend each day gazing,
At wondrous sunsets over glorious horizons.

My Digital Universe, Portfolio

On the mind

Cheran Ratnam

Hearing is involuntary but listening is a choice. You don’t have to listen to all the voices that you hear. Exercise control when it comes to who and what you authorize to influence your mind.

Yes, you are the gatekeeper of your mind.

The senses are mere receptacles. Your mind has the power to decide whether to push play or pause. What about the involuntary noises? Exercise control and selectivity. In order to do this effectively and consistently, one must come to the realization that the soul itself is the controller of your life.

The soul is not a control freak, it’s there for a purpose but it won’t execute that purpose without willingness. If the purpose of the soul is to create, it will wait till the vessel (you) are ready both in your mind and body. You hold the key to your soul, mind and body.

Mind and perception

Perhaps we forget that we are not our body, we are not our mind, we are not our soul … we are but one and all. What you can comprehend is what you will perceive. From what you perceive you will derive your filtered version of comprehension and from that comprehension you will execute actions that will create your experiences. Not a single piece of the universe is out of place. If it was, life will not be sustainable. So, be selective. Life is infinite. Possibilities are endless.

“Make your dreams out of rubber balls, they won’t drown.

If you throw it away, it will bounce back!

Rubber balls are fun, not just for you but for others too.”

My Digital Universe

When expectations fetter away

Cheran Ratnam

At the end of the day, one must do what’s best for themselves without becoming a thorn for anothers’ possibilities and expressions. Those who demand you to meet their expectations will never change to meet yours. So, why waste time trying to meet the expectations of those who are concerned with propagating their version of life on to your reality?

Expectations create dependancies

If all expectations were to be fulfilled, the world might end up in chaos. To expect is to wait for something. You are now dependent on something else to find fulfillment. Like a mighty pit bull, salivating and kneeling before his feeble master for a treat. I love dogs, but I am not too fond of training them to beg for treats. The loyalty of a dog is more valuable than manipulative obedience.

Or, to expect is to project your perception of ideal outcomes onto other people’s lived experiences and expressions. They are not meeting YOUR expectations that YOU made up in YOUR head with YOUR self-interests at the forefront.

Water is fluid. Most of everything around us consists of it. The same element that we consume to hydrate can drown us. Just like that, that which goes up, must come down. That which stays up must one day land. There’s nothing wrong with going up or staying up as long as one remembers where it started from. Born naked with the imminent reality of joining the dust, some get too caught up in their self-proclaimed status on the planet. This self-proclaimed superiority complex then starts to ooze out like poisonous gas in the form of expectations. If you were a bird, your innate dispositions would tell you when to land. But you aren’t a bird. You are just a skeleton with a suit with nothing but expectations.

Expectations are not universal laws

Tell your mind to guard its entrance from unwelcomed expectations. No matter how high and mighty they present themselves as, they are nothing but expectations of dust. The deeply rooted fear of uncertainty is only a facade when you are not afraid to take bold steps. Not all steps are taken to go forward. There’s no loss in taking a step back to avoid an imminent threat and change directions. The forces that work behind closed doors are not powerful. They are just fear mongers that needs a hand to hold when it’s dark at night. With light, each and every plot will be revealed. Such a revelation can only become a reality by the hand of the absolute truth. Oil and water will get separated then and there won’t be any more doubts.

In the meantime,  it’s better to show less than what you know. Don’t worry about anothers’ perception of yourself. When you are able to free yourself from the false notion of external acceptance, freedom will take you ashore with powerful waves. Till then, ride the waves and enjoy the sunsets. Expect nothing and know when to give it all and when to give sparingly. When we expect nothing, we are not controlled by that idea. When you free yourself from your own shackles, you will find the strength to remain free.

About the photo: These waves in Nusa Lembongan Island in Indonesia are simply breathtaking. Its shear power keeps the mighty rocks that keep the island afloat in check.

 

 

 

My Digital Universe, Poems

When the sun fell in love with the moon

Cheran Ratnam

He rose up early and painted beautiful skies,
Even for a few minutes, he tried!
He stayed up as late as he could at night,
Even when he was drowning,
He tried to shed some light.

Then it dawned on him:
It’s his arrival that makes the moon go away,
And entire planets depended on his arrival.
With sadness and without choice,
He decided to forever paint the skies for the moon 🌙 
Hoping she will realize why he has to go away every night. 
~Instagram.com/smilander87 

http://lankansapphires.com/ has some beautiful moonstones and a variety of natural gemstones. 

Follow them on Instagram at: Instagram.com/ LankanSapphires 

My Digital Universe, Poems

Getting old 

Cheran Ratnam

When the tea becomes cold,
And there’s no hand to hold,
You are now getting old;
No point doing as your’re told.

You realize there’s a hole,
As you search within your soul;
Like a fisherman with no pole,
You keep fighting for your goal.

Some just want to be a mole,
Watch out! They will take a toll.
Those who are looking for coal,
Use the kind-hearted to maul.

Don’t treat me like some doll,
Or a cat with a plastic ball.
I don’t have to wait for your call;
Or for Saul to change into Paul.

My Digital Universe, Poems

Any given day 

Cheran Ratnam

Always keep things simple,
And remember to be nimble.
It won’t pay, but stay humble,
Care not much about a dimple;
Reality is more about the pimple.

Do things that may seem ridiculous,
Not to you, but for the superfluous.
Not for their sake: live scrupulous,
Follow your passions and be meticulous.

You’ll find many smiles to be dubious, 
That’s not your problem, Mr. studious!
Pay less attention to things too fabulous, 
Be careful with time, when your laborious.

Whatever it is, that helps you sleep,
Shouldn’t bother your neighbor, please!
Spread jam on toast not hate on prose,
We can only handle each other in a dose. 

Poems, Portfolio

Beautiful confinement.

Cheran Ratnam

Beautiful confinement...

A box that contain memories, of bittersweet realities,
The only thing that knows the inner walls of a desolate heart.
No one can open it- there is no key,
As the heart closes its eyes to embrace its forlorn reality,
Memories flood into the box painting painfully beautiful imagery.

At the core of the box lies a garden- exclusive,
Where the mind can create memories all-inclusive,
Not bound by that which bounds the external realm,
The mind admires beauty and wanders off.

Invisible but strong, undetectable yet true,
Beyond emotions, gestures and fantasies or any poetic imagery,
Desires whisper secrets that acknowledgment will never hear,
Wordless melodies, utterly meaningless to the world outside the box,
Not out of fear, rather,to avoid subjugation of inadequate means,
The only visitor and retriever…keeps the box alive.

Not all explanations are created equal. For an explanation to be useful in practice, it must do more than highlight inputs or display weights. It needs to behave reliably and reflect how the model actually works.
Academic Writing, Data Science, My Digital Universe, Portfolio

Why Consistency and Faithfulness Matter in AI Explanations

Cheran Ratnam

In the growing field of explainable AI, tools like LIME and SHAP have made it possible to peek inside complex models and understand their reasoning. But just because a model can explain itself doesn’t mean every explanation is meaningful or trustworthy.

Evaluating the Quality of Explanations

Not all explanations are created equal. For an explanation to be useful in practice, it must do more than highlight inputs or display weights. It needs to behave reliably and reflect how the model actually works.

Two critical properties help assess that:

1. Consistency

A good explanation should behave consistently. That means:

  • If you train the same model on different subsets of similar data, the explanations should remain relatively stable.
  • Small changes to input data shouldn’t lead to dramatically different explanations.

Inconsistent explanations can confuse users, misrepresent what the model has learned, and signal overfitting or instability in the model itself.

2. Faithfulness

Faithfulness asks a simple but powerful question: Do the features highlighted in the explanation actually influence the model’s prediction?

An explanation is not faithful if it attributes importance to features that, when changed or removed, don’t affect the outcome. This kind of misleading output can erode trust and create false narratives around how the model operates.

Why These Metrics Matter

In sensitive applications like healthcare, lending, or security, misleading explanations are more than just technical flaws. They can have real-world consequences.

  • Imagine a credit scoring model that cites a user’s browser history or favorite color as key decision drivers. Even if the model is technically accurate, such explanations would damage its credibility and raise ethical and legal concerns.
  • In regulated industries, explanations that fail consistency or faithfulness checks can expose organizations to compliance risks and reputational damage.

Real-World Examples

Faithfulness Test: Credit Risk Model

A faithfulness test was applied to a credit risk model used to classify applicants as “high” or “low” risk. The SHAP explanation highlighted feature A (e.g., number of bank accounts) as highly important.

To test faithfulness, this feature was removed and the model’s prediction didn’t change … at all!

What the graph shows:

  • SHAP value for “Number of Bank Accounts” was +0.25 (suggesting a major contribution).
  • But after removing it, the model’s risk prediction stayed the same, proving that this feature wasn’t actually influencing the output.

This revealed a serious problem: the model was producing unfaithful explanations. It was surfacing irrelevant features as important, likely due to correlation artifacts in the training data.

Consistency Test: Credit Risk Model

A credit scoring model was trained on two different but similar subsets of loan application data. Both versions produced the same prediction for an applicant: “high risk”, but gave very different explanations.

What the graph shows:

  • In Training Set A, the top contributing feature was “Credit Utilization” (+0.3).
  • In Training Set B, it was “Employment Type” (+0.28).
  • The SHAP bar charts for the same applicant looked noticeably different, even though the final decision didn’t change.

This inconsistency raised questions about model stability: Can we trust that the model is learning the right patterns, or is it too sensitive to the training data?

Final Thoughts

As AI systems continue to make critical decisions in our lives, explainability is not a luxury, it’s a necessity. Tools like LIME and SHAP offer a valuable window into how models work, but that window needs to be clear and reliable.

Metrics like consistency and faithfulness help us evaluate the strength of those explanations. Without them, we risk mistaking noise for insights, or worse, making important decisions based on misleading information.

Accuracy might get a model deployed, but consistency and faithfulness should decide its validity and trust. If you want to learn more about explainability in AI, please check this blog post, where I talk about how LIME and SHAP can help explain model outcomes.

Understanding Model Decisions with SHAP and LIME
Academic Writing, Data Science, My Digital Universe

What Made the Model Say That? Real Examples of Explainable AI

Cheran Ratnam

When people talk about artificial intelligence, especially deep learning, the conversation usually centers around accuracy and performance. How well does the model classify images? Can it outperform humans in pattern recognition? While these questions are valid, they miss a crucial piece of the puzzle: explainability.

Explainability is about understanding why an AI model makes a specific prediction. In high-stakes domains like healthcare, finance, or criminal justice, knowing the why is just as important as the what. Yet this topic is often overlooked in favor of performance benchmarks.

Why Is Explainability Hard in Deep Learning?

Classical models like decision trees (e.g., CART) offer built-in transparency. You can trace the decision path from root to leaf and understand the model’s logic. But deep learning models are different. They operate through layers of nonlinear transformations and millions of parameters. As a result, even domain experts can find their predictions opaque.

This can lead to problems:

  • Lack of trust from users or stakeholders
  • Difficulty debugging or improving models
  • Potential for hidden biases or unfair decisions

This is where explainability tools come in.

Tools That Help Open the Black Box

Two widely used frameworks for model explanation are LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). Both aim to provide insights into which features influenced a specific prediction and by how much.

LIME in Action

LIME works by perturbing the input data and observing how the model’s predictions change. For instance, in a text classification task, LIME can highlight which words in an email led the model to flag it as spam. It does this by creating many variations of the email (e.g., removing or replacing words) and observing the output.

Loan Risk Example:

  • A model classifies a loan application as risky. We will use John as an example.
  • We want to find the reasons as to why their application was labeled as risky.
  • LIME reveals that the applicant’s job status and credit utilization were the two most influential factors.

LIME reveals that the model flagged John’s loan as risky mainly due to his contract employment status and high credit utilization. Although John had no previous defaults and a moderate income, those factors were outweighed by the others in the model’s decision.

SHAP in Practice

SHAP uses concepts from cooperative game theory to assign each feature an importance value. It ensures a more consistent and mathematically grounded explanation. SHAP values can be plotted to show how each input feature pushes the prediction higher or lower.

Medical Diagnosis Example:

  • Let’s use Maria as an example, after her information was entered into the system, she was classified as high risk by the model
  • To understand as to what factors contributed to that classification, we can use SHAP. SHAP shows that age and blood pressure significantly contributed to a high-risk prediction.
  • These insights help physicians verify if the model aligns with clinical reasoning.

Final Thoughts

The examples of Maria and John illustrate a powerful truth: even highly accurate models are incomplete without explanations. When a model labels someone as high-risk, whether for a disease or a loan default, it’s not enough to accept the outcome at face value. We need to understand why the model made that decision.

Tools like LIME and SHAP make this possible. They open up the black box and allow us to see which features mattered most, giving decision-makers the context they need to trust or challenge the model’s output.

Why Explainability Matters in Business:

  • Builds trust with stakeholders
  • Supports accountability in sensitive decisions
  • Uncovers potential biases or errors in the model
  • Aligns predictions with domain expertise

As AI becomes more embedded in real-world systems, explainability is not optional; it’s essential. It turns predictions into insights, and insights into informed, ethical decisions.

Good AI model evaluations doesn’t stop at explainability. Learn the importance of consistency and faithfulness and see why it matters by checking out this post.

The value of traditional language corpora for pretraining LLMs is plateauing, making it necessary to gather new, challenging data to improve performance on language and reasoning tasks.
Academic Writing, Data Science, Portfolio

Agentic LLMs: The Evolution of AI in Reasoning and Social Interaction

Cheran Ratnam

The landscape of artificial intelligence is evolving every second. Large Language Models (LLMs) are evolving from passive entities into active, decision-making agents. This shift introduces agentic LLMs. Now, we have all seen people metnion agentic this, agentic that, the last few months. In essence, these systems are endowed with reasoning abilities, interfaces for real-world action, and the capacity to engage with other agents. These advancements are poised to redefine industries such as robotics, medical diagnostics, financial advising, and scientific research.

The Three Pillars of Agentic LLMs

  1. Reasoning Capabilities At the heart of agentic LLMs lies their reasoning ability. Drawing inspiration from human cognition, these systems emulate both rapid, intuitive decisions (System 1 thinking) and slower, analytical deliberation (System 2 thinking). Current research predominantly focuses on enhancing the decision-making processes of individual LLMs.
  2. Interfaces for Action Moving beyond static responses, agentic LLMs are equipped to act within real-world environments. This is achieved through the integration of interfaces that facilitate tool usage, robotic control, or web interactions. Such systems leverage grounded retrieval-augmented techniques and benefit from reinforcement learning, enabling agents to learn through interaction with their environment rather than relying solely on predefined datasets.
  3. Social Environments The third component emphasizes multi-agent interaction, allowing agents to collaborate, compete, build trust, and exhibit behaviors akin to human societies. This fosters a social environment where agents can develop collective intelligence. Concepts like theory of mind and self-reflection enhance these interactions, enabling agents to understand and anticipate the behaviors of others.

A Self-Improving Loop

The interplay between reasoning, action, and interaction creates a continuous feedback loop. As agents engage with their environment and each other, they generate new data for ongoing training and refinement. This dynamic learning process addresses the limitations of static datasets, promoting perpetual improvement.

Here, agents act in the world, generate their own experiences, and learn from the outcomes, without needing a predefined dataset. This approach, used by models from OpenAI and DeepSeek, allows LLMs to capture the full complexity of real-world scenarios, including the consequences of their own actions. Although reinforcement learning introduces challenges like training instability due to feedback loops, these can be mitigated through diverse exploration and cautious tuning. Multi-agent simulations in open-world environments may offer a more scalable and dynamic alternative for generating the diverse experiences required for stable, continual learning.

From Individual Intelligence to Collective Behavior

The multi-agent paradigm extends the focus beyond individual reasoning to explore emergent behaviors such as trust, deception, and collaboration. These dynamics are observed in human societies, and insights gained from these studies can inform discussions on artificial superintelligence by modeling how intelligent behaviors emerge from agent interactions.

Conclusion

Agentic LLMs are reshaping the understanding of machine learning and reasoning. By enabling systems to act autonomously and interact socially, researchers are advancing toward creating entities capable of adaptation, collaboration, and evolution within complex environments. The future of AI lies in harmonizing these elements: reasoning, action, and interaction into unified intelligent agent systems that not only respond but also comprehend, decide, and evolve.

What does this mean for fine tuning LLMs? Well, here is where it gets interesting. Unlike traditional LLM fine-tuning, which relies on static datasets curated from the internet and shaped by past human behavior, agentic LLMs can generate new training data through interaction. This marks a shift from supervised learning to a self-learning paradigm rooted in reinforcement learning.

For an indepth take on agentic LLMs, I highly recommend reading this survey.

LLMS thinking
Data Science, My Digital Universe, Portfolio

What Large Language Models Really Are: Not Minds, Just Math with Tools

Cheran Ratnam

LLMs like ChatGPT are often described as if they think. They don’t. At least not qiote like humans. Which, may not be a bad thing given the kind of stuff us humans have conjured up over the years.

Back in 2011, Daniel Kahneman introduced System 1 and System 2 thinking:

  • System 1: fast, intuitive, automatic
  • System 2: slow, deliberate, reasoned

LLMs are pure System 1 engines. They don’t reason. They don’t understand. They predict the next token … that’s it.

Every “intelligent” response is just a string of highly probable guesses. Step by step, word by word. Recent research on Agentic LLMs make this a tad bit interesting.

By plugging LLMs into tools for reasoning, retrieval, symbolic logic, interaction, we build the appearance of System 2 thinking:

  • Step-by-step prompting
  • Calling calculators or search engines
  • Planning with external tools
  • Interacting with other agents

This isn’t true deliberation. It’s orchestration. We’re layering deliberate behavior on top of probabilistic word prediction.

The magic of modern LLMs isn’t intelligence. It’s composition, blending fast token prediction with structured workflows and external tools. They’re not minds. They’re language interfaces made powerful through math, scale, and tool use.

Understanding this doesn’t undercut them. It makes us better at using them.

Diagram illustrating how a large language model (LLM) answers questions using ontology embeddings, Chain-of-Thought prompting, and Retrieval-Augmented Generation from a knowledge graph.
Academic Writing, Data Science, My Digital Universe, Portfolio

Revolutionizing Data Interaction: How AI Can Comprehend Your Evolving Data Without Retraining

Cheran Ratnam

In the rapidly evolving landscape of enterprise AI, organizations often grapple with a common challenge: enabling large language models (LLMs) to interpret and respond to queries based on structured data, such as knowledge graphs, without necessitating frequent retraining as the data evolves.

A novel approach addresses this issue by integrating three key methodologies:

  1. Ontology embeddings : Transform structured data into formats that LLMs can process, facilitating an understanding of relationships, hierarchies, and schema definitions within the data.
  2. Chain-of-Thought prompting: Encourage LLMs to engage in step-by-step reasoning, enhancing their ability to navigate complex data structures and derive logical conclusions.
  3. Retrieval-Augmented Generation (RAG): Equip models to retrieve pertinent information from databases or knowledge graphs prior to generating responses, ensuring that outputs are both accurate and contextually relevant.

By synergizing these techniques, organizations can develop more intelligent and efficient systems for querying knowledge graphs without the need for continuous model retraining.

Implementation Strategy

  • Combining Ontology Embeddings with Chain-of-Thought Prompting: This fusion allows LLMs to grasp structured knowledge and reason through it methodically, which is particularly beneficial when dealing with intricate data relationships.
  • Integrating within a RAG Framework: Traditionally used for unstructured data, RAG can be adapted to retrieve relevant segments from knowledge graphs, providing LLMs with the necessary context for informed response generation.
  • Facilitating Zero/Few-Shot Reasoning: This approach minimizes the need for retraining by utilizing well-structured prompts, enabling LLMs to generalize across various datasets and schemas effectively.

Organizational Benefits

Adopting this methodology offers several advantages:

  • Reduced Need for Retraining: Systems can adapt to evolving data without the overhead of continuous model updates.
  • Enhanced Explainability: The step-by-step reasoning process provides transparency in AI-driven decisions.
  • Improved Performance with Complex Data: The model’s ability to comprehend and navigate structured data leads to more accurate responses.
  • Adaptability to Schema Changes: The system remains resilient amidst modifications in data structures.
  • Efficient Deployment Across Domains: LLMs can be utilized across various sectors without domain-specific fine-tuning.

Practical Applications

This approach has been successfully implemented in large-scale systems, such as the Dutch national cadastral knowledge graph (Kadaster), demonstrating its viability in real-world scenarios. For instance, deploying a chatbot capable of:

  • Understanding domain-specific relationships without explicit programming.
  • Updating its knowledge base in tandem with data evolution.
  • Operating seamlessly across departments with diverse taxonomies.
  • Delivering transparent and traceable answers in critical domains.

Conclusion

By integrating ontology-aware prompting, systematic reasoning, and retrieval-enhanced generation, organizations can develop AI systems that interact with structured data more effectively. This strategy not only streamlines the process but also enhances the reliability and adaptability of AI applications in data-intensive industries. For a comprehensive exploration of this methodology, refer to Bolin Huang’s Master’s thesis.

A visual representation of a Knowledge Graph Question Answering (KGQA) framework that integrates ontology embeddings, Chain-of-Thought prompting, and Retrieval-Augmented Generation (RAG). The diagram shows the flow from user query to LLM reasoning and response generation based on structured data from a knowledge graph.