DEV Community: Sanjay Batthula

Your AI Is Accurate. But Is It Fair, Ethical, or Even Trustworthy?

Sanjay Batthula — Sun, 18 Oct 2026 09:00:34 +0000

Your AI Is Accurate. But Is It Fair, Ethical, or Even Trustworthy?

Why building responsible AI requires more than high accuracy, sophisticated algorithms, and good intentions.

Imagine two candidates applying for the same job.

Both have relevant qualifications, strong experience, and similar skills. An AI-powered recruitment system evaluates their applications and recommends one candidate while rejecting the other.

The organisation reports that its model achieves 95% accuracy.

Sounds impressive, right?

But what if that same system consistently makes more mistakes when evaluating candidates from certain demographic groups?

What if applicants never discover why they were rejected?

And what if nobody can meaningfully challenge the decision?

Suddenly, 95% accuracy doesn't tell us nearly enough.

This is one of the most important questions in AI development: when a system produces technically impressive results, does that automatically make it fair, ethical, or trustworthy?

Not necessarily.

Through my studies in AI & Data Ethics as part of my MSc in Applied Artificial Intelligence at Northeastern University London, I've been exploring how these questions connect to real-world AI systems.

The most interesting lesson is that AI ethics isn't simply about making algorithms better. It's about understanding what makes their use justified, who their decisions affect, and how those decisions should be governed.

Let's explore some assumptions that deserve closer examination.

1. Can an AI System Be Accurate and Still Be Unfair?

Absolutely. And this is where evaluating AI becomes more complicated than checking a single performance metric.

Imagine an AI system used to assess the risk of loan defaults. It achieves the same overall accuracy for two demographic groups.

At first glance, the system might appear equally effective for everyone.

But suppose its mistakes are distributed differently:

For Group A, it frequently rejects applicants who would actually repay their loans.
For Group B, it more frequently approves applicants who subsequently default.

Even if overall accuracy is identical, the groups experience different types of errors and consequences.

A false rejection could deny someone access to housing, education, or a business opportunity. A false approval creates a different kind of financial risk.

The ethical question isn't only how often the AI is correct. It's also who bears the consequences when it is wrong.

A real-world example: COMPAS

The COMPAS risk-assessment system became a prominent example in discussions about algorithmic fairness.

In 2016, ProPublica examined the system's predictions about the likelihood of defendants being arrested again.

Its analysis reported racial differences in the types of errors made. Black defendants were more likely to be incorrectly classified as higher risk, while white defendants were more likely to be incorrectly classified as lower risk.

The company behind COMPAS disputed ProPublica's analysis and defended the system using a different interpretation of predictive fairness.

The disagreement highlights something important: different fairness criteria can lead to different conclusions about the same model.

An AI system can perform similarly across groups under one measure and differently under another.

Fairness is more than a metric

In Getting from Commitment to Content in AI and Data Ethics: Justice and Explainability, John Basl, Ronald Sandler, and Steven Tiell discuss why broad commitments to justice need more precise meaning before they can guide AI development.

Three useful perspectives are:

Procedural justice: Is the decision-making process fair, consistent, and open to appropriate review?

Distributive justice: Are benefits, opportunities, risks, and burdens distributed fairly?

Recognition justice: Are affected individuals and communities properly recognised, respected, and represented?

These perspectives help explain why optimising a fairness metric is not the same thing as resolving every fairness concern.

An AI hiring system, for example, might satisfy a statistical criterion while still relying on an unjustified process or making decisions that applicants cannot challenge.

The lesson for developers is straightforward:

Before asking which fairness metric to optimise, ask what fairness should mean in the particular system you're building.

2. If an AI Explains Its Decision, Does That Make It Trustworthy?

Suppose an AI system rejects a loan application.

The applicant asks why.

The system responds:

"Your application was rejected because of your credit history, income stability, and debt-to-income ratio."

That sounds transparent.

But does the explanation establish that the decision was justified?

Not necessarily.

Perhaps the underlying data was inaccurate. Perhaps an inappropriate variable influenced the prediction. Perhaps the applicant has no way to correct an error.

This brings us to four related but distinct concepts.

Transparency concerns what information about the system and its operation is made available.

Explainability concerns whether understandable reasons or accounts can be provided for a system's behaviour.

Interpretability concerns how people can make sense of a model's operation or outputs.

Accountability concerns who must answer for decisions, address failures, and provide appropriate remedies.

These concepts are connected, but they aren't interchangeable.

A system may disclose technical details without offering an explanation that a person can understand.

It may provide a convincing explanation without showing whether the underlying decision was justified.

And it may have detailed records of every decision while lacking a meaningful process for challenging mistakes.

This is closely connected to Basl, Sandler, and Tiell's argument that ethical principles need context-specific requirements.

Simply promising transparency is insufficient.

We should ask:

Transparent to whom?
About which information?
For what purpose?
What can an affected person actually do with that information?

Consider the difference between giving someone a technical explanation and giving them a genuine opportunity to challenge an incorrect outcome.

The second requires more than a well-written explanation. It may require access to relevant information, a review process, and someone with the authority to correct the decision.

Transparency can help enable accountability, but transparency alone does not guarantee it.

3. If You Agree to Data Collection, Is Your Privacy Protected?

We've all clicked "Accept" on privacy notices without reading every line.

But does clicking a button always mean we have made a meaningful, informed choice?

Imagine an AI personal assistant that manages your calendar.

To schedule a meeting, it requests access to your calendar, email, contacts, and documents.

Some of that information may be useful for completing the task.

But should the assistant automatically have permission to access every message, retain unrelated information, or share another person's contact details?

This reveals a key distinction:

Authorising an AI system to complete a task is not the same as giving it unrestricted authority over personal data.

Privacy is more than keeping information secret

Privacy also involves autonomy, control, context, and exposure to potential harm.

A service might securely store information while collecting substantially more than it needs.

A person might agree to data processing without having a meaningful alternative.

And data shared for one legitimate purpose might later be reused in ways that the person did not reasonably expect.

This is why privacy cannot be reduced to either encryption or a consent checkbox.

The seven UK GDPR principles

The UK General Data Protection Regulation provides an important foundation for thinking about responsible personal-data processing.

Its seven principles are:

Lawfulness, fairness, and transparency: Process personal data lawfully and fairly, with appropriate transparency.
Purpose limitation: Collect data for specified, legitimate purposes and respect restrictions on further use.
Data minimisation: Collect only data that is necessary for the relevant purpose.
Accuracy: Keep personal data accurate and appropriately updated.
Storage limitation: Avoid retaining identifiable personal data longer than necessary.
Integrity and confidentiality: Protect information using appropriate security measures.
Accountability: Take responsibility for complying with data protection requirements and demonstrate that compliance.

These principles are relevant when developing AI systems that access, process, store, or share personal information.

One important legal distinction: consent is not the only lawful basis for processing personal data under the UK GDPR.

The broader principle is that processing must have an appropriate lawful basis and comply with the applicable requirements.

For developers, a useful question is not simply:

"Can my AI system access this data?"

It is:

"Does this system need the data, is its use justified, and what controls protect the people concerned?"

4. Are Algorithms Showing Us What We Want, or Shaping What We Believe?

Consider how you consume information online.

You open a social platform and encounter a stream of posts, articles, recommendations, and opinions.

You might assume that what you see mainly reflects your interests.

But the information you encounter is also influenced by the platform's ranking systems, recommendation objectives, and the behaviour of other users.

That matters because the information we encounter can influence the beliefs and decisions we form.

Why misinformation spreads

In Information Overload Helps Fake News Spread, and Social Media Knows It, Filippo Menczer and Thomas Hills examine how information overload, cognitive biases, social influence, and algorithmic amplification contribute to misinformation.

Several mechanisms are especially interesting.

Information overload: People cannot carefully evaluate every piece of information they encounter.

Confirmation bias: We may be more receptive to information that supports beliefs we already hold.

In-group influence: Information shared by people we identify with may seem more credible.

Algorithmic amplification: Ranking and recommendation systems can increase the visibility of particular content, including misleading material.

Social bots: Automated accounts can contribute to the appearance and spread of online social activity.

These mechanisms can interact.

Imagine someone encounters a misleading claim, engages with it, receives more related content, and repeatedly encounters similar messages from trusted contacts.

Over time, the repeated exposure may reinforce existing beliefs.

Importantly, this is a possible feedback mechanism, not an inevitable outcome for every user.

When does personalisation become manipulation?

This question becomes even more interesting when considered alongside Will Democracy Survive Big Data and Artificial Intelligence? by Dirk Helbing and colleagues.

Their discussion examines how large-scale data collection, behavioural prediction, and automated influence can affect individual autonomy and democratic decision-making.

Personalisation can be useful.

It can help people discover relevant information, reduce unnecessary searching, and navigate large amounts of content.

But it can also create opportunities to steer behaviour.

The ethical distinction involves more than whether a recommendation is personalised.

We should consider whether people understand how they are being influenced, whether relevant alternatives are concealed, and whether systems exploit vulnerabilities rather than support informed choices.

A recommendation system doesn't need to issue an explicit instruction to influence a decision.

Sometimes, changing which options a person sees is enough to change the decision they eventually make.

This is why responsible AI includes attention to the wider information environment, not just the predictive accuracy of individual models.

5. When AI Causes Harm, Who Is Responsible?

Suppose an organisation deploys an AI system that automatically evaluates customer requests.

One day, it rejects an eligible customer because of incorrect data.

The developer says the model produced its expected output.

The organisation says the system made the decision automatically.

The customer receives an explanation but cannot find anyone authorised to correct the mistake.

So who is responsible?

This illustrates the difference between identifying a technical failure and establishing accountability.

A technical investigation might determine that the problem originated in the input data, model predictions, decision rules, or integration with another system.

But identifying the cause doesn't automatically tell us who had a duty to prevent the harm or who must correct it.

Responsibility cannot simply disappear into automation

Developers, deploying organisations, managers, and users may have different responsibilities depending on the system and context.

For example:

Developers may be responsible for implementing agreed technical requirements, testing behaviour, and communicating limitations.
Deploying organisations may be responsible for deciding whether the system is appropriate for its intended use, establishing oversight, and handling complaints.
Authorised human operators may have responsibilities relating to review, intervention, and escalation.

The exact allocation depends on the circumstances, contractual relationships, governance arrangements, and applicable law.

What about AI agents?

This question becomes especially relevant when AI systems do more than generate predictions.

An AI agent might retrieve information, select tools, interact with external services, and carry out several steps toward a goal.

An agent may successfully complete an authorised task while taking an unnecessary or inappropriate action along the way.

For example, consider an assistant asked to arrange a meeting. It successfully schedules the appointment but also discloses confidential information from an unrelated document.

The final task appears completed.

But the overall behaviour is not acceptable.

This connects AI ethics with practical engineering decisions, including tool permissions, access restrictions, human approvals, execution records, and mechanisms for stopping or correcting actions.

A system may have a human approval button and still provide ineffective oversight if the reviewer lacks sufficient information or authority.

Meaningful human oversight requires the ability to understand, intervene, challenge, and correct decisions where appropriate.

6. Can Ethical Principles Actually Become Engineering Requirements?

Many AI organisations describe their systems using familiar commitments:

Fairness. Transparency. Privacy. Accountability. Safety.

These are worthwhile goals.

But saying that a system is committed to fairness does not tell engineers which outcomes should count as unfair.

Promising privacy does not specify what data should be collected, retained, or deleted.

And declaring accountability does not establish who will investigate incidents or provide remedies.

This is the central challenge raised in Basl, Sandler, and Tiell's work: moving from broad ethical commitments to meaningful, context-specific requirements.

In other words, AI ethics needs to influence what gets built, tested, deployed, and governed.

From values to practical safeguards

Consider an AI recruitment system.

Ethical concern	Practical question	Possible response
Fairness	Are errors distributed unjustifiably across applicant groups?	Evaluate relevant group-level performance and review outcomes
Privacy	Is unnecessary personal information being collected?	Apply data minimisation and access restrictions
Transparency	Can applicants understand relevant aspects of the decision?	Provide accessible explanations and appropriate information
Accountability	Who can investigate and correct a rejection?	Define review, escalation, and appeal procedures
Autonomy	Is meaningful human judgment preserved where needed?	Establish effective human review and intervention

These examples are starting points, not universal solutions.

A fairness metric appropriate for one application might be unsuitable for another.

Greater transparency may need to be balanced against legitimate privacy and security concerns.

Collecting additional data might improve certain evaluations while creating new privacy risks.

And human review can become symbolic if reviewers merely approve automated recommendations without meaningful scrutiny.

Responsible AI therefore involves identifying trade-offs, making defensible decisions, and examining whether safeguards work as intended.

Ethics is not a final-stage checklist

A more useful approach is to integrate ethical reasoning throughout the AI system lifecycle.

Before development: Ask whether the intended use is justified, who will be affected, and what harms are foreseeable.

During design: Translate relevant values into specific requirements for data, model behaviour, permissions, explanations, and oversight.

During evaluation: Test not only aggregate performance but also relevant error patterns, limitations, and potential harms.

During deployment: Monitor behaviour, clarify responsibilities, and provide appropriate channels for intervention and correction.

After deployment: Reassess the system when its data, use, operating environment, or effects change.

This is also where the relationship between AI Ethics and AI Engineering becomes particularly important.

Ethical reasoning helps identify what ought to matter.

Engineering provides ways to implement, evaluate, and improve systems in response to those concerns.

Neither can replace the other.

7. So, What Makes an AI System Trustworthy?

Let's return to the recruitment system from the beginning.

It achieves 95% accuracy.

After exploring fairness, explainability, privacy, autonomy, and accountability, we can see why that statistic doesn't settle whether the system should be used.

We would still need to ask:

Are the system's errors acceptable and fairly distributed?
Was the data collected and processed appropriately?
Can affected people understand and challenge consequential decisions?
Does the system support meaningful human judgment?
Are responsibilities for failures clearly established?
Can the organisation monitor, correct, or discontinue the system when necessary?

These questions don't necessarily have simple or universally applicable answers.

That is precisely why ethical reasoning matters.

A trustworthy AI system is not defined by accuracy alone, nor by a checklist of ethical promises. Trustworthiness requires evidence that the system is appropriate for its purpose, that relevant risks are understood and managed, and that people remain able to hold its operators accountable.

For developers working in Applied AI, this changes how we think about building intelligent systems.

The objective is not merely to produce outputs that look correct.

It is to build systems whose decisions, limitations, and real-world consequences can be understood, evaluated, and responsibly managed.

The most important question may not be:

"How accurate is our AI?"

But rather:

"What makes this AI system worthy of the trust we're asking people to place in it?"

AI Agents vs Chatbots: They're Not the Same Thing

Sanjay Batthula — Wed, 05 Aug 2026 11:06:55 +0000

A few months ago, if someone mentioned AI, most people immediately thought of ChatGPT.

Today, the conversation has changed.

Everywhere you look, people are talking about AI Agents.

Tech companies are building them.

Startups are raising millions around them.

Developers are integrating them into products.

And suddenly, everyone claims that "AI Agents are the future."

But here's the interesting part.

Many people still use the terms chatbot and AI agent as if they mean the same thing.

They don't.

In fact, understanding the difference might be one of the most valuable concepts to learn if you're exploring the future of Artificial Intelligence.

So let's break it down.

The AI We Know Today

Imagine opening ChatGPT.

You ask,

"Write a professional email."

It writes one.

You ask,

"Explain neural networks."

It explains them.

You ask,

"Debug my Python code."

It helps you fix it.

Every interaction follows the same pattern.

You ask.

It responds.

Then it waits.

That's how a chatbot works.

It is reactive.

It responds only when you ask it to do something.

Once it finishes the task, its job is done.

Now Imagine Something Different

Instead of asking ChatGPT to plan your vacation step by step...

Imagine telling an AI,

"Plan my five-day trip to Tokyo under $2,000."

Without asking another question, it begins working.

It searches for flights.

Compares hotel prices.

Checks the weather forecast.

Creates a daily itinerary.

Calculates your budget.

Suggests restaurants.

Adjusts the schedule if flights become unavailable.

Finally, it presents everything in one organized plan.

You didn't guide every step.

You simply gave it a goal.

That's much closer to how an AI agent works.

The Biggest Difference

The easiest way to understand it is this.

A chatbot completes tasks.

An AI agent pursues goals.

A chatbot waits for instructions after every response.

An AI agent decides what the next step should be.

It's the difference between asking someone for directions and hiring someone to plan your entire journey.

How AI Agents Actually Work

Although every system is different, most AI agents follow a similar cycle.

Step 1: Understand the Goal

Instead of focusing only on your latest message, the agent first identifies what you're trying to achieve.

For example,

Build a website.

Analyze sales data.

Schedule meetings.

Create a marketing campaign.

The objective becomes the center of its decision-making.

Step 2: Break the Goal Into Smaller Tasks

Humans naturally solve large problems by dividing them into manageable pieces.

Modern AI agents attempt to do the same.

If the goal is:

Launch an online store.

The agent might create subtasks like:

Research competitors
Design the website
Generate product descriptions
Build the checkout system
Test the application
Deploy it online

Rather than waiting for each instruction, it builds its own workflow.

Step 3: Use Tools

This is where things become powerful.

Unlike traditional chatbots that only generate text, AI agents can interact with external tools.

Depending on their capabilities, they might:

Search the web
Read documents
Execute code
Query databases
Send emails
Create spreadsheets
Schedule meetings
Generate reports

The language model becomes the brain.

The tools become the hands.

Step 4: Evaluate the Results

Suppose an agent searches for information but doesn't find enough.

Instead of stopping immediately, it may decide to search again using a different strategy.

It evaluates its own progress.

If something fails, it can often try another approach.

This ability to iterate makes agents feel much more autonomous than traditional chatbots.

A Simple Analogy

Imagine you're managing a restaurant.

A chatbot is like a waiter.

You ask for water.

It brings water.

You ask for the menu.

It brings the menu.

You ask for the bill.

It brings the bill.

Every action depends on your request.

An AI agent is more like the restaurant manager.

It notices inventory running low.

Orders new supplies.

Schedules employees.

Tracks customer feedback.

Updates pricing.

Optimizes operations.

It doesn't simply answer questions.

It works toward keeping the restaurant running smoothly.

Are AI Agents Smarter Than Chatbots?

Not necessarily.

Many AI agents actually use Large Language Models like ChatGPT under the hood.

The intelligence often comes from how they're organized rather than having a completely different brain.

Think of it like this.

A brilliant chef and a restaurant owner may both understand cooking.

The difference is that the owner coordinates people, resources, schedules, and decisions to achieve a larger objective.

Similarly, AI agents combine reasoning with planning, memory, and tools.

Where We'll See AI Agents

We're already seeing early versions of AI agents across industries.

In software development, agents can generate code, run tests, identify bugs, and suggest improvements.

In healthcare, they can help organize patient information and assist clinicians by summarizing complex medical records.

Businesses are using agents to automate customer support, analyze sales trends, and generate reports.

Researchers are experimenting with agents that search scientific literature, compare findings, and organize information for further analysis.

These systems aren't replacing human expertise.

They're becoming collaborative partners that handle repetitive or time-consuming work.

Does This Mean Chatbots Will Disappear?

Not at all.

Chatbots remain incredibly useful.

Sometimes, all you need is a quick answer, a code explanation, or help drafting an email.

In those situations, a chatbot is the perfect tool.

AI agents become valuable when solving larger, multi-step problems that require planning, decision-making, and interaction with external systems.

It's not a competition.

It's about choosing the right tool for the right task.

The Bigger Picture

The shift from chatbots to AI agents represents something much bigger than a new technology trend.

It reflects a change in how we interact with computers.

For decades, software waited for humans to tell it exactly what to do.

Now, we're beginning to build systems that can understand objectives, create plans, and execute parts of those plans with minimal supervision.

That's a profound change.

It doesn't mean humans are becoming less important.

It means our role is evolving from directing every action to defining meaningful goals and making informed decisions.

Final Thoughts

Chatbots started the conversation.

AI agents are changing the way work gets done.

One responds to prompts.

The other works toward outcomes.

Both are powered by remarkable advances in Artificial Intelligence, but they solve different kinds of problems.

As AI continues to evolve, understanding this distinction won't just help you follow the latest trends.

It will help you build better systems, ask better questions, and imagine entirely new ways of solving real-world challenges.

And perhaps that's the most exciting part.

We're still at the beginning.

Thanks for reading!

Why Large Language Models Hallucinate (And Why That Isn't a Bug)

Sanjay Batthula — Tue, 28 Jul 2026 14:35:11 +0000

A few days ago, I asked ChatGPT for information about a research paper.

It confidently explained the paper.

It even included the authors.

The publication year.

The methodology.

Everything looked convincing.

There was just one problem.

The paper didn't exist.

At first, it feels strange.

How can one of the most advanced AI systems ever built make up information with such confidence?

Is it broken?

Is it lying?

Or is something much deeper happening beneath the surface?

The answer changes the way you'll think about Artificial Intelligence forever.

The Biggest Misconception About ChatGPT

Most people imagine ChatGPT as a super-fast search engine.

Ask a question.

Search a gigantic database.

Find the correct answer.

Return it.

That isn't what happens.

Large Language Models don't search through a hidden encyclopedia every time you ask a question.

They generate language.

That's a huge difference.

Imagine asking your friend to finish this sentence.

"The capital of France is..."

Almost everyone immediately says "Paris."

Not because they searched a database.

Because their brain predicts the most likely continuation based on everything they've learned.

Large Language Models work similarly.

They're incredibly sophisticated prediction engines.

Every word they generate is simply the next most probable token given everything that came before it.

So... What Is a Hallucination?

A hallucination occurs when an AI generates information that sounds believable but isn't factually correct.

It might invent:

Research papers that were never published
Statistics that don't exist
Fake quotations
Incorrect citations
Imaginary software functions
Historical events that never happened

The scary part isn't that it's wrong.

The scary part is how believable it sounds.

Because the model is optimized to generate fluent language, not guaranteed truth.

Fluency and factual accuracy are not the same thing.

Why Isn't This Considered a Bug?

Imagine asking someone to continue this sentence.

"Every morning I drink a cup of..."

Most people would answer:

"Coffee."

But maybe the correct answer was:

"Green tea."

You weren't wrong because your brain malfunctioned.

You simply predicted the most likely continuation.

Large Language Models do exactly the same thing.

Except instead of relying on years of personal experience, they rely on patterns learned from enormous amounts of text.

Sometimes those patterns align perfectly with reality.

Sometimes they don't.

Hallucinations aren't software crashes.

They're an expected consequence of how these models generate language.

Where Hallucinations Usually Happen

Hallucinations become more common when the model doesn't have enough reliable information.

For example:

Asking About Extremely Recent Events

If something happened only hours ago, the model may not have enough context to answer accurately.

Requesting Highly Specific Facts

Inventing a page number.

Creating a fake DOI.

Guessing a research citation.

These are situations where prediction can drift away from reality.

Ambiguous Questions

If your prompt leaves room for interpretation, the model has to make assumptions.

Sometimes those assumptions are wrong.

Why Confidence Can Be Misleading

Humans often associate confidence with correctness.

AI doesn't.

A language model can produce an answer that sounds incredibly certain because confidence isn't something it "feels."

It's simply generating text that statistically fits the conversation.

The words may sound authoritative.

That doesn't automatically make them true.

This is why critical thinking becomes one of the most valuable skills in the age of AI.

Can Hallucinations Be Reduced?

Yes.

Not eliminated entirely.

But significantly reduced.

Here are a few practical ways.

Ask Better Questions

Instead of asking:

Explain quantum computing.

Try:

Explain quantum computing for a beginner using simple analogies and mention any limitations of your explanation.

Clear prompts reduce ambiguity.

Verify Important Information

For anything involving:

Medical advice
Legal information
Financial decisions
Academic citations
Research papers

Always verify using trusted sources.

AI should assist your thinking, not replace it.

Provide Context

Instead of asking:

Summarize this topic.

Ask:

Summarize this article using only the information provided below.

The more context the model has, the less it needs to guess.

Use External Knowledge

Modern AI systems often combine Large Language Models with external databases and search systems.

This approach, known as Retrieval-Augmented Generation (RAG), allows the model to retrieve relevant information before generating a response.

Instead of relying only on learned patterns, it grounds its answers in real documents.

While this doesn't eliminate hallucinations completely, it significantly improves factual accuracy for many tasks.

Does This Mean AI Can't Be Trusted?

Not at all.

Think of AI like an exceptionally fast research assistant.

It can help brainstorm ideas.

Explain difficult concepts.

Generate code.

Summarize long documents.

Translate languages.

Write reports.

But just as you would review work prepared by a human assistant, you should also review important AI-generated information.

The goal isn't blind trust.

It's effective collaboration.

The Bigger Picture

One of the most fascinating aspects of Artificial Intelligence is that its greatest strength and its greatest weakness come from the same mechanism.

The ability to generate creative, natural, human-like language is exactly what allows it to occasionally generate incorrect information.

Prediction creates intelligence.

Prediction also creates hallucinations.

Understanding this trade-off helps us become better AI users.

Instead of asking,

"Can AI ever be wrong?"

We begin asking,

"How can I work with AI responsibly?"

That question is far more valuable.

Final Thoughts

Hallucinations aren't proof that AI is failing.

They're proof that AI works differently from how many people imagine.

Large Language Models don't retrieve facts the way search engines do.

They generate the most likely continuation based on patterns learned during training and the context available in the conversation.

Most of the time, those predictions are remarkably useful.

Sometimes, they're confidently incorrect.

The future isn't about replacing human judgment with AI.

It's about combining human reasoning with AI's incredible ability to process and generate information.

The more we understand how these systems work, the better we'll become at using them wisely.

Thanks for reading!

What Actually Happens When You Ask ChatGPT a Question? A Step-by-Step Journey Inside an LLM

Sanjay Batthula — Wed, 15 Jul 2026 11:22:26 +0000

A visual, step-by-step journey through the technology powering modern Large Language Models.

You type a question into ChatGPT.

Five seconds later, you get a response that feels almost human.

It explains concepts.

Writes code.

Solves math.

Debates philosophy.

Even cracks jokes.

It feels like someone on the other side of the screen is thinking.

But here's the surprising part.

ChatGPT doesn't think the way humans do.

It doesn't understand language in the same way your brain does.

It doesn't search through a mental filing cabinet.

It doesn't pause to reflect on your question before answering.

Instead, beneath that simple text box, billions of mathematical operations race through layers of neural networks in milliseconds.

Your words become numbers.

Numbers become vectors.

Vectors become probabilities.

Probabilities become language.

By the time you finish reading this sentence, your prompt has already traveled through one of the most sophisticated engineering systems humanity has ever built.

So what actually happens after you press Enter?

Let's go inside the machine.

The Roadmap

Your Question
      │
      ▼
1. Tokenization
      │
      ▼
2. Embeddings
      │
      ▼
3. Positional Information
      │
      ▼
4. Transformer Layers
      │
      ▼
5. Self-Attention
      │
      ▼
6. Feed Forward Networks
      │
      ▼
7. Next Token Prediction
      │
      ▼
8. Sampling
      │
      ▼
9. Response Generation

Every answer the AI gives follows this journey.

Let's walk through it together.

Step 1. Tokenization

Why doesn't ChatGPT read words?

Imagine walking into a library where every book has been shredded into tiny pieces.

Not random pieces.

Smart pieces.

Some are complete words.

Some are prefixes.

Some are suffixes.

Some are punctuation.

Some are even spaces.

That's how ChatGPT reads language.

Not as sentences.

Not even as words.

It reads tokens.

A token is simply a chunk of text.

For example, when you type:

Explain recursion to me.

The model might internally split it into something like:

"Explain"

↓

"rec"

↓

"ursion"

↓

"to"

↓

"me"

↓

"."

Those pieces are then converted into numerical IDs.

The exact IDs vary depending on the tokenizer, but the idea stays the same.

By the time the neural network sees your prompt, it has never seen the words.

Only numbers.

Why tokenize?

Because computers don't understand English.

They understand integers.

Tokenization is the bridge between human language and mathematics.

Step 2. Embeddings

How do numbers become meaning?

Suppose I gave you two numbers.

3812

7421

Do they mean anything?

Not at all.

They're just IDs.

So the model performs another transformation.

Every token ID becomes a long list of floating-point numbers called an embedding.

Imagine every word in every language living inside a gigantic multidimensional map.

On this map:

Dog is close to puppy.
King is close to queen.
Paris is close to France.
Apple (the fruit) sits somewhere different from Apple (the company).

Instead of storing dictionary definitions, the model learns where concepts belong relative to one another.

For example:

Dog

↓

[0.18, -1.24, 0.91, ... 12,288 numbers]

These vectors aren't human-readable.

Yet somehow they capture meaning.

Words with similar meanings naturally end up near each other.

The model doesn't memorize definitions.

It learns relationships.

Why embeddings?

Because mathematics works on vectors, not vocabulary.

Step 3. Positional Information

If words become vectors, how does the AI know their order?

Imagine someone gives you these words:

cat

dog

chased

the

You know the words.

But not the sentence.

Now compare:

The dog chased the cat.

and

The cat chased the dog.

Same words.

Completely different meaning.

Since embeddings alone don't preserve order, the model adds positional information.

Every token receives a mathematical signal describing where it appears in the sentence.

Explain → Position 1

rec → Position 2

ursion → Position 3

to → Position 4

me → Position 5

Now the AI knows both what each token is and where it belongs.

Without positional information, every sentence would become a bag of shuffled words.

Step 4. Transformer Layers

Where does the actual intelligence happen?

Imagine a factory with dozens of production lines.

Your prompt enters one end.

Each production line improves the representation a little.

Layer after layer.

Each one refines meaning.

Finds relationships.

Resolves ambiguity.

Builds understanding.

Modern language models don't have just a few layers.

They have dozens.

Sometimes well over a hundred.

Every layer asks a slightly different question.

What matters here?

Which words relate?

What pattern am I seeing?

By the time your prompt exits the final layer, it has been analyzed from hundreds of different perspectives.

Step 5. Self-Attention

How does ChatGPT know which words matter?

This is the invention that changed everything.

Imagine you're reading this sentence:

The trophy didn't fit in the suitcase because it was too small.

What was too small?

The trophy?

Or the suitcase?

You instantly know it was the suitcase.

Not because the last word said so.

Because your brain connected ideas across the sentence.

Self-attention lets AI do something similar.

Every token can "look at" every other token.

Not equally.

It learns which ones deserve attention.

For example:

The trophy didn't fit in the suitcase because it was too small.

When processing it, the model assigns more attention to suitcase than trophy.

Attention isn't fixed.

It changes for every sentence.

Every prompt.

Every token.

This dynamic focus is why modern AI can understand long contexts far better than earlier language models.

Why attention?

Because meaning depends on relationships, not just individual words.

Step 6. Feed Forward Networks

Once the model gathers context, what happens next?

Think of self-attention as gathering information from across the room.

The feed forward network is where each token quietly thinks about what to do with that information.

Every token passes through a small neural network.

Not once.

At every transformer layer.

These networks perform nonlinear mathematical transformations that gradually reshape each token's representation.

This is where abstract concepts emerge.

Programming patterns.

Grammar.

Logic.

Reasoning shortcuts.

Writing style.

The model isn't storing explicit rules.

It's learning mathematical patterns that behave like rules.

Step 7. Next Token Prediction

Does ChatGPT know the whole answer before it starts typing?

Surprisingly...

No.

It predicts only one token at a time.

Suppose you've written:

The capital of France is

The model calculates probabilities.

Paris      98.7%

London      0.3%

Berlin      0.2%

Pizza       0.0001%

It selects one.

Then the sentence becomes:

The capital of France is Paris

Now it predicts the next token.

Maybe:

.

↓

and

↓

which

↓

,

Then repeats.

Again.

And again.

Until the response is complete.

This happens astonishingly fast.

Dozens of predictions every second.

Thousands for longer answers.

Your conversation is literally built one token at a time.

Step 8. Sampling

If the model always chooses the most likely token, why aren't all responses identical?

Imagine asking a musician to play Happy Birthday.

If they always chose the single most predictable note, every performance would sound identical.

Instead, language models use sampling.

Rather than always picking the highest probability token, they sometimes consider several highly likely options.

This controlled randomness makes responses feel more natural.

It's also why asking the same question twice can produce slightly different answers.

Settings like temperature control this behavior.

Lower temperatures make responses more deterministic.

Higher temperatures encourage creativity and exploration.

Too high, though, and coherence begins to fall apart.

Step 9. Response Generation

How does text finally appear on your screen?

The chosen token is converted back into text.

3812

↓

"Explain"

Then another.

Thousands of tiny predictions become paragraphs.

Your browser streams them as they're generated.

That typing animation isn't fake.

The model genuinely doesn't know the final sentence until it has predicted every token before it.

The answer is literally being constructed in real time.

Why Does ChatGPT Sometimes Hallucinate?

If it's so advanced...

Why does it confidently invent facts?

The answer lies in how it works.

ChatGPT is not retrieving facts from a hidden encyclopedia.

It is predicting what token is statistically most likely to come next based on patterns learned during training and the information available in the current conversation.

Most of the time, those patterns align with reality.

Sometimes they don't.

When the model lacks enough reliable information, it can still generate fluent, convincing language.

Fluency isn't the same as factual accuracy.

That's why a response can sound incredibly confident while being completely wrong.

The model is optimizing for plausible continuation, not guaranteed truth.

Is There a Tiny Database Hidden Inside?

People often imagine a little library buried somewhere inside the model.

A giant encyclopedia.

A dictionary.

A search engine.

Or maybe a tiny digital person making decisions.

None of those exist inside the core neural network.

There are no stored definitions saying:

Paris = Capital of France

There are no folders labeled:

Physics

History

Programming

Mathematics

Instead, knowledge is distributed across billions of learned parameters.

Each parameter is just a number.

Individually, they're meaningless.

Together, they encode an astonishing web of relationships learned from vast amounts of text.

No single neuron contains the answer.

The answer emerges from all of them working together.

Why Does This Feel Like Intelligence?

Because prediction, repeated billions of times, starts to resemble reasoning.

The model recognizes patterns.

Maintains context.

Follows instructions.

Generalizes from examples.

Writes coherent arguments.

Explains unfamiliar ideas.

Solves problems it has never seen word-for-word.

From the outside, it feels like thinking.

Inside, it's mathematics unfolding at extraordinary scale.

The Twist

After everything we've explored, here's the part that surprises most people.

The strangest thing about ChatGPT isn't that it can answer questions.

The strangest thing is what isn't inside it.

There is no tiny dictionary.

No database of facts.

No miniature librarian searching for answers.

No hidden search engine.

No little person making decisions.

Just billions of learned mathematical relationships predicting one token after another.

And somehow...

Prediction becomes explanation.

Prediction becomes creativity.

Prediction becomes code.

Prediction becomes conversation.

That may be one of the most remarkable engineering achievements of our time.

So the next time you press Enter, remember this:

You're not simply sending words to a machine.

You're triggering a cascade of mathematics that transforms numbers into language, one prediction at a time.

And for a few seconds, that cascade becomes a conversation.

The AI Revolution Is Already Here. Are We Ready for It?

Sanjay Batthula — Fri, 03 Jul 2026 16:12:43 +0000

Artificial Intelligence isn't knocking on our door anymore. It's already inside our homes, workplaces, classrooms, and even our daily conversations. The real question is no longer whether AI will change our lives. It's whether we're ready to change with it.

We Didn't Notice the Revolution

Think about your day for a moment.

You unlock your phone using facial recognition.

Google Maps predicts traffic before you leave.

Netflix recommends the next show you'll probably binge.

Your bank detects suspicious transactions in seconds.

You ask ChatGPT or Gemini to summarise documents, write emails, debug code, or explain concepts.

None of these experiences feel extraordinary anymore.

That's exactly what makes this revolution different.

Unlike previous technological breakthroughs that arrived with obvious fanfare, artificial intelligence has quietly woven itself into the fabric of our everyday lives.

The revolution didn't begin when ChatGPT went viral.

It began years earlier, and most of us were already participating without realising it.

AI Is Changing More Than Technology

Artificial Intelligence isn't just another software upgrade.

It's reshaping how entire industries operate.

Healthcare is diagnosing diseases faster.

Financial institutions are detecting fraud in real time.

Manufacturing is reducing waste through predictive maintenance.

Developers are writing software alongside AI-powered coding assistants.

Students are learning with personalised tutors available 24/7.

Businesses are automating repetitive work so people can focus on solving bigger problems.

This isn't about replacing humans.

It's about redefining how humans create value.

*The Biggest Change Isn't Technical. It's Personal.
*
Every major technological shift has rewarded people who adapted early.

The internet rewarded those who learned to use it.

Smartphones rewarded those who embraced mobility.

Cloud computing rewarded those who understood scalability.

Artificial Intelligence is no different.

The people who thrive over the next decade won't necessarily be the ones who know everything about AI.

They'll be the ones who learn how to collaborate with it.

Knowing how to ask better questions, validate AI-generated answers, think critically, and solve real problems will become just as valuable as technical expertise itself.

Progress Comes With Responsibility

Of course, every powerful technology brings new challenges.

AI raises important questions about privacy, misinformation, bias, copyright, security, and ethics.

These aren't problems we should ignore.

There are problems we need to solve responsibly.

Innovation without responsibility creates risk.

Responsibility without innovation slows progress.

The future depends on finding the right balance between the two.

The Opportunity Ahead

We're witnessing one of the most significant technological transformations of our generation.

Some people see uncertainty.

Others see opportunity.

Personally, I see a chance to learn.

Not because AI will magically solve every problem.

But because understanding AI is becoming as fundamental as understanding the internet became twenty years ago.

We don't all need to become AI researchers.

But we do need to understand the tools that are shaping the world we'll live and work in.

Final Thoughts

The AI revolution isn't a headline from the future.

It's happening right now.

Every day, another workflow changes.

Another industry evolves.

Another problem becomes solvable in ways that seemed impossible only a few years ago.

The question isn't whether AI will shape the future.

It already is.

The real question is whether we'll choose to grow alongside it.

Thanks for reading.

If this article made you think, I'd love to hear your perspective.

How has AI changed your work, studies, or everyday life? Share your thoughts in the comments. Let's start a conversation.

DEV Community: Sanjay Batthula

Your AI Is Accurate. But Is It Fair, Ethical, or Even Trustworthy?

Your AI Is Accurate. But Is It Fair, Ethical, or Even Trustworthy?

1. Can an AI System Be Accurate and Still Be Unfair?

A real-world example: COMPAS

Fairness is more than a metric

2. If an AI Explains Its Decision, Does That Make It Trustworthy?

3. If You Agree to Data Collection, Is Your Privacy Protected?

Privacy is more than keeping information secret

The seven UK GDPR principles

4. Are Algorithms Showing Us What We Want, or Shaping What We Believe?

Why misinformation spreads

When does personalisation become manipulation?

5. When AI Causes Harm, Who Is Responsible?

Responsibility cannot simply disappear into automation

What about AI agents?

6. Can Ethical Principles Actually Become Engineering Requirements?

From values to practical safeguards

Ethics is not a final-stage checklist

7. So, What Makes an AI System Trustworthy?

Further Reading

AI Agents vs Chatbots: They're Not the Same Thing

The AI We Know Today

Now Imagine Something Different

The Biggest Difference

How AI Agents Actually Work

Step 1: Understand the Goal

Step 2: Break the Goal Into Smaller Tasks

Step 3: Use Tools

Step 4: Evaluate the Results

A Simple Analogy

Are AI Agents Smarter Than Chatbots?

Where We'll See AI Agents

Does This Mean Chatbots Will Disappear?

The Bigger Picture

Final Thoughts

Why Large Language Models Hallucinate (And Why That Isn't a Bug)

The Biggest Misconception About ChatGPT

So... What Is a Hallucination?

Why Isn't This Considered a Bug?

Where Hallucinations Usually Happen

Asking About Extremely Recent Events

Requesting Highly Specific Facts

Ambiguous Questions

Why Confidence Can Be Misleading

Can Hallucinations Be Reduced?

Ask Better Questions

Verify Important Information

Provide Context

Use External Knowledge

Does This Mean AI Can't Be Trusted?

The Bigger Picture

Final Thoughts

What Actually Happens When You Ask ChatGPT a Question? A Step-by-Step Journey Inside an LLM

You type a question into ChatGPT.

The Roadmap

Step 1. Tokenization

Why doesn't ChatGPT read words?

Why tokenize?

Step 2. Embeddings

How do numbers become meaning?

Why embeddings?

Step 3. Positional Information

If words become vectors, how does the AI know their order?

Step 4. Transformer Layers

Where does the actual intelligence happen?

Step 5. Self-Attention

How does ChatGPT know which words matter?

Why attention?

Step 6. Feed Forward Networks

Once the model gathers context, what happens next?

Step 7. Next Token Prediction

Does ChatGPT know the whole answer before it starts typing?

Step 8. Sampling

If the model always chooses the most likely token, why aren't all responses identical?

Step 9. Response Generation

How does text finally appear on your screen?

Why Does ChatGPT Sometimes Hallucinate?

Is There a Tiny Database Hidden Inside?

Why Does This Feel Like Intelligence?