How ChatGPT / Gemini think? (simple flow)

Neenga ChatGPT la "Tell me a joke" nu type panreenga. 2 seconds la oru joke varudhu. But background la enna nadakkudhu?

Billions of calculations. Trillions of parameters. Massive neural networks. Complex probability distributions.

Scary ah irukka? Don't worry — indha article la idha simple ah, brain analogy use panni explain pannrom.

Think about it — unga brain eppadi velai seyyudhu? Yaaro "How are you?" nnu kekkaanga, neenga automatically "I'm fine" nu solreenga. Yosikka koodla illai — pattern ah irukku.

ChatGPT um Gemini um exactly idha dhan panraanga — but trillion times more data la irundhu patterns learn pannirukkanga.

Indha article la neenga purinjukuvaanga:

• AI "thinking" process step by step
• Tokens na enna, eppadi velai seyyudhu
• Temperature and creativity settings
• ChatGPT vs Gemini — internal differences

Ready to peek inside the AI brain? Let's go! 🧠

First things first — AI actually "think" pannadhu. Idhu oru common misconception.

Human Thinking vs AI "Thinking":

So AI enna pannum?

AI oru super advanced autocomplete maadiri velai seyyudhu. Unga phone keyboard la "Good" type panreenga, phone "morning", "night", "job" suggest pannum — keyboard padam irukka? Same concept, but million times more sophisticated.

ChatGPT / Gemini oru word generate panna, billions of mathematical operations nadakkum. But fundamental ah, adhu "next word enna varum?" nu predict panradhu dhan.

Example:

• Input: "The capital of India is ___"
• AI predicts: "New" (99.8% probability)
• Then: "Delhi" (99.9% probability)

Simple ah sollanumna: AI = World's smartest autocomplete that learned from the entire internet 🌐

AI text process panna tokens use pannum. Idhu oru crucial concept.

Tokens na enna?

Token = word or word piece. AI text ah words ah padikka maataadhu — tokens ah padikkum.

Examples:

• "I love AI" = 3 tokens: ["I", "love", "AI"]
• "Programming" = 1 token
• "Unbelievable" = 3 tokens: ["Un", "believ", "able"]
• "ChatGPT is amazing" = 4 tokens

Yean tokens?

• Computers ku words puriyaadhu — numbers dhan puriyum
• Each token ku oru number assign aagum (like a dictionary index)
• AI numbers la dhan calculations pannum
• Numbers ah vapas words ah convert pannum

Token Flow — Step by Step:

1. Input Tokenization: Unga prompt tokens ah split aagum
2. Embedding: Each token ku oru number vector assign aagum (like coordinates in space)
3. Attention: AI decide pannum — enda tokens important, enda tokens related
4. Processing: Neural network layers through pass aagum (GPT-4 la 120+ layers!)
5. Prediction: Next token enna varum nu probability calculate aagum
6. Selection: Highest probability token select aagum
7. Repeat: Process repeat aagum until response complete

Token Limits:

• GPT-4: ~128,000 tokens (oru novel size!)
• Gemini Pro: ~1,000,000 tokens
• Claude: ~200,000 tokens

Token limit exceed aanaaa — AI response cut aagum or error varum! 📏

Temperature AI response oda creativity control pannum. Idhu oru super important concept.

Imagine pannunga: Neenga oru restaurant la irukeenga. Menu la 10 items irukku.

• Temperature 0 (Very Low): Always same item order pannuvenga — most popular dish. Predictable, safe.
• Temperature 0.5 (Medium): Usually popular items, sometimes try something new.
• Temperature 1.0 (High): Random ah enna venumnaalum order pannuvenga — adventurous!

AI la eppadi velai seyyudhu:

Practical Example:

Prompt: "Complete this: The sky is ___"

• Temperature 0.1: "blue" (every time same answer)
• Temperature 0.5: "blue" / "clear" / "beautiful" (varies slightly)
• Temperature 0.9: "blue" / "painted with dreams" / "crying tonight" (creative!)

Where to set temperature?

• ChatGPT: Automatic (you can't directly set it in free version)
• API usage: You can set temperature parameter
• Gemini: Automatic, but "be creative" type prompts implicitly increase it

Pro tip: Factual questions ku low temperature best. Creative tasks ku high temperature best. Neenga prompt la "be creative" or "be precise" nu solla, AI adjust pannidum! 🎨

Both ChatGPT and Gemini "think" similarly, but key differences irukku:

How they "think" differently:

ChatGPT approach:

"Oru question vandha, naan learn panna patterns la irundhu best answer construct panren" — like a brilliant writer who crafts responses.

Gemini approach:

"Oru question vandha, naan Google oda knowledge + my training combine panni answer tharen" — like a research assistant with Google access.

Real-world difference:

Prompt: "What happened in India today?"

• ChatGPT: "I don't have access to today's news..." (unless browsing enabled)
• Gemini: Actually searches Google and gives current info!

Prompt: "Write a creative story about a robot"

• ChatGPT: Usually more creative, better narrative flow
• Gemini: Good but slightly more factual/structured

Bottom line: Different tools for different jobs. Both try pannunga, compare pannunga! 🤝

AI thinking process real examples la paakalaam:

Example 1: Simple Factual Question

• Prompt: "Capital of Japan?"
• AI Process: High confidence → "Tokyo" (probability 99.9%)
• Temperature effect: Same answer every time
• Tokens used: Input: 4, Output: 1

Example 2: Creative Writing

• Prompt: "Write a haiku about coding"
• AI Process: Multiple valid answers possible → temperature matters
• Low temp: "Lines of code flow / Debugging through the night sky / Program runs at dawn"
• High temp: "Silicon dreams dance / Bugs whisper through midnight screens / Coffee fuels the void"
• Tokens used: Input: 6, Output: ~20

Example 3: Complex Reasoning

• Prompt: "Explain why the sky is blue in Tamil"
• AI Process:

1. Understand: science question + Tamil language
2. Retrieve: Rayleigh scattering knowledge
3. Translate: concepts to Tamil
4. Generate: simplified Tamil explanation

• Tokens used: Input: 10, Output: ~200

Example 4: Code Generation

• Prompt: "Python function to reverse a string"
• AI Process: Very structured, low temperature internally
• Output: Clean, working code with comments
• Tokens used: Input: 8, Output: ~50

Notice the pattern: Simple questions = fast, confident. Complex/creative tasks = more processing, more variation. Adhu dhan AI thinking! 🧠

AI Brain = World's Biggest Library + Super Smart Librarian 📚

Imagine pannunga — oru massive library irukku. 100 billion books irukku. Every subject, every language, every topic covered.

Anga oru super smart librarian irukkaaru. Avar ella books um padichirukkaru. Perfect memory irukku.

Neenga avarkitta kekkureenga: "Tell me about black holes in simple words"

Librarian enna pannuvaru?

1. Relevant books identify panuvaru — physics, astronomy, popular science

2. Simple language la irukkura books prefer panuvaru — "in simple words" nu sonneenga la

3. Multiple sources combine panuvaru — one book la irundhu definition, another la irundhu analogy

4. Pudhu explanation create panuvaru — copy paste panna maataaru, own words la soluvaru

Idhu exactly ChatGPT / Gemini panradhu:

- Library = Training data (internet, books, code)

- Librarian's memory = Model parameters (GPT-4 la 1.7 trillion parameters!)

- Your question = Prompt

- Librarian's answer = Generated response

Key difference: Real librarian ku oru book edukkavae 5 minutes aagum. AI "librarian" ku? Milliseconds!

Athanala dhan AI ippo ithanai powerful ah irukku — speed + knowledge = 🔥

ChatGPT um Gemini um Transformer architecture use pannum. Idhu 2017 la Google invent panna architecture — AI world ah completely maathiduchu.

Transformer oda magic: Self-Attention

Imagine pannunga indha sentence: "The cat sat on the mat because it was tired"

"it" yaara refer panradhu? Cat ah? Mat ah?

Humans instantly purinjukuvanga — "it" = cat. Because cats get tired, mats don't.

Self-Attention mechanism exactly idha dhan pannum:

• Each word ku weight assign pannum (enna words related?)
• "it" ku "cat" high weight, "mat" low weight
• Context correctly understand pannum

Why Transformer is special:

Before Transformers (RNN/LSTM):

• Words ah one-by-one sequentially process pannum
• Slow, long sentences la context maranthidum
• Like reading a book one word at a time, forgetting the beginning

After Transformers:

• All words simultaneously process pannum (parallel processing!)
• Attention mechanism — any word, any other word ah relate panna mudiyum
• Like seeing the whole page at once and understanding connections

GPT = Generative Pre-trained Transformer

• Generative: Content create pannum
• Pre-trained: Already massive data la trained
• Transformer: Indha architecture use pannum

Transformer invention — arguably 2017 oda most important tech breakthrough. Google researchers publish panna "Attention Is All You Need" paper — history maathina paper! 📜

AI thinking process therinjaa, better prompts ezhudhalaam:

Practical applications:

1. Better Prompts:

Now you know AI predicts tokens → Give clear, specific prompts with context. More context = better prediction = better output.

2. Managing Expectations:

AI "thinks" differently → Don't expect human-like reasoning. Expect pattern-based responses.

3. Debugging Bad Outputs:

Response wrong? → Maybe ambiguous prompt (AI chose wrong pattern). Rephrase with more context.

4. Choosing Right Tool:

Need real-time info? → Gemini (has Google Search). Need creative writing? → ChatGPT. Need long document analysis? → Claude.

5. Cost Optimization (API users):

Tokens = money → Shorter prompts save tokens → Save costs. But too short = bad quality. Balance important! ⚖️

AI "thinking" la key limitations:

1. No True Reasoning 🧩

AI pattern match pannum, reason pannadhu. "2+2=?" ku answer theriyum (pattern), but novel math problems la fail aagalaam.

2. Context Window Limits 📏

Long conversations la earlier messages "maranthidum". GPT-4 128K tokens, but very long chats la still context miss aagum.

3. Training Data Cutoff 📅

ChatGPT's training data oru specific date la mudinju pochu. Recent events theriyaadhu (unless browsing enabled).

4. Inconsistency 🔄

Same question ku different answers varum (temperature effect). Factual queries ku idhu problematic.

5. Can't Learn from You 🚫

Neenga conversation la correct pannaalum, AI permanently learn aagaadhu. Next session la same mistake pannalaam.

6. Multimodal Gaps 🖼️

Image understanding improving but not perfect. Complex charts, handwriting, memes la mistakes varum.

Key takeaway: AI is a tool, not a thinker. Powerful tool — but still a tool. Neenga dhan the real thinker! 🧠

"Oru tool eppadi velai seyyudhu nu therinjaa, adha better ah use pannalaam" — idhu universal truth.

Mechanic vs Driver analogy:

• Driver: Car start pannum, drive pannum, petrol podum
• Mechanic: Engine eppadi velai seyyudhu, enna problem vandha enna pannanum theriyum

AI use pannura ellarum "drivers." But AI thinking purinjukuravanga "mechanics." Avanga:

✅ Better prompts ezhudhuvaanga — AI eppadi process pannum nu theriyum

✅ Faster debugging — wrong output vandha yean nu purinjukuvaanga

✅ Right tool choose pannivaanga — task ku edhu best nu theriyum

✅ Realistic expectations — AI enna panna mudiyum, mudiyaadhu nu theriyum

✅ Career advantage — interviews la "how does AI work?" nu kekkuranga

2026 Job Market Reality:

• "Can you use ChatGPT?" — Basic question, everyone can
• "Do you understand HOW ChatGPT works?" — This separates beginners from experts

Indha article padicheenga na, neenga already better understand panreenga. Next time someone asks "how does ChatGPT work?", neenga confidently explain pannalaam — tokens, transformers, attention, temperature — ella keywords um unga kitta irukku! 💪

✅ AI actually "think" pannadhu — mathematically next token predict pannum

✅ Tokens = words/word pieces. AI text ah tokens ah process pannum

✅ Transformer architecture = Self-attention mechanism — words relationships understand pannum

✅ Temperature controls creativity: Low = factual, High = creative

✅ ChatGPT = better creative writing | Gemini = better research + real-time info

✅ Context window = AI oru time la handle panna mudinja text amount (tokens la measure)

✅ AI "thinking" purinjaa → better prompts, better outputs, better career

✅ Self-Attention = AI oda secret weapon — which words relate to which words nu understand pannum

✅ AI is a powerful tool, not a thinker — you are the real brain! 🧠

Challenge: AI Thinking Explorer! 🔬

Task 1: Temperature Test

Same prompt 5 times try pannunga: "Give me a creative business name for a tea shop in Chennai"

• How many different answers varudhu?
• Which ones are creative, which ones are boring?

Task 2: Token Awareness

ChatGPT la idha try pannunga:

• Short prompt: "Explain AI"
• Long prompt: "Explain AI for a 20-year-old engineering student in Chennai who knows basic programming but nothing about machine learning. Use Tamil analogies."
• Compare outputs — which is better? Why?

Task 3: Context Experiment

• First message: "What's the best food?"
• Second message: "What about for breakfast?"
• Third message: "And for someone with diabetes?"
• See how AI remembers context and refines answers!

Task 4: Model Comparison

Same prompt in ChatGPT AND Gemini: "Summarize today's top 3 tech news"

• Which one gives current info?
• Which one gives better analysis?

Share your findings! Understanding HOW AI thinks makes you a power user 🚀

Q1: Explain how ChatGPT generates a response.

A: ChatGPT uses a Transformer architecture. When you send a prompt, it's tokenized into smaller pieces, converted to numerical embeddings, processed through multiple attention layers that understand word relationships, and then generates the response token-by-token by predicting the most probable next token.

Q2: What is the Transformer architecture?

A: Transformer is a neural network architecture introduced in 2017 ("Attention Is All You Need" paper). Its key innovation is the self-attention mechanism that lets the model weigh relationships between all words simultaneously, unlike previous sequential models (RNN/LSTM).

Q3: What is temperature in language models?

A: Temperature is a parameter that controls the randomness of the model's output. Low temperature (0.1-0.3) makes outputs more deterministic and focused. High temperature (0.7-1.0) increases randomness and creativity. Temperature 0 always picks the highest probability token.

Q4: What are tokens and why do they matter?

A: Tokens are the basic units AI models process — they can be words, subwords, or characters. They matter because: models have token limits (context window), API pricing is per token, and understanding tokenization helps write better prompts.

Q5: How is ChatGPT different from Gemini architecturally?

A: ChatGPT uses GPT (decoder-only transformer) optimized for text generation. Gemini uses an encoder-decoder architecture designed for multimodal inputs (text, image, audio, video). Gemini also has native Google Search integration for real-time information.

"Understanding your tools makes you a craftsman, not just a user."

Ippo neenga therinjukkitteenga — ChatGPT and Gemini eppadi "think" panraanga. Tokens, transformers, attention, temperature — ella concepts um clear ah irukku.

Indha knowledge unga AI usage ah next level ku eduthukum. Neenga ippo just "user" illa — neenga oru informed user. Better prompts ezhudhuvenga, wrong outputs debug pannivaanga, right tool choose pannivaanga.

Fun fact: Indha article AI thinking pathi explain panradhu, but indha article ezhudha koodah AI use pannirukkalam 😄 That's the beauty of 2026!

Next article la — LLM (Large Language Model) na enna nu deep ah paakalaam. Transformer oda foundation — neural networks, training process, GPT vs Gemini comparison — ellam varum!

Keep learning, keep experimenting! 🚀

📖 Previous: [What is Generative AI?](/bytes/genai/01-what-is-generative-ai) — GenAI basics recap

📖 Next Byte: [What is LLM? (brain analogy)](/bytes/genai/03-what-is-llm) — Neural networks, training, GPT vs Gemini deep comparison

📖 Then: [Prompt vs Normal Question](/bytes/genai/04-prompt-vs-normal-question) — Better prompts ezhudha techniques

📖 Finally: [First Prompt → First AI Output](/bytes/genai/05-first-prompt-first-ai-output) — 5 copy-paste prompts for hands-on practice

Series progress: 2/5 complete! Keep going 💪