A lot of people think:
"ChatGPT is like a giant database that stores all answers."
That is wrong.
A better mental model:
ChatGPT is a very large pattern-learning machine that predicts and generates text.
Let's break this down.
1. What is an LLM?
LLM stands for:
Large Language Model
Let's split the words.
Large
Because these models contain billions of parameters.
A parameter is basically a value the model adjusts during learning.
Example:
10 million parameters
A large model:
100+ billion parameters
More parameters generally allow the model to learn more complex patterns.
Language
It works with human language.
It learns relationships between:
- words
- sentences
- concepts
- writing styles
- programming code
Example:
King → Queen
Man → Woman
Java → Programming Language
Model
A model is just a mathematical system that takes input and produces output.
Input
|
↓
Model
|
↓
Output
2. How does an LLM learn?
Imagine teaching a child English.
You don't give them a dictionary and say:
Memorize everything.
You expose them to examples.
Same idea.
Step 1: Collect huge amounts of text
Examples:
- books
- websites
- articles
- code
- documents
Basically, a huge collection of language examples.
Step 2: Convert text into numbers
Computers don't understand:
"Hello, how are you?"
They understand numbers.
So text is converted into tokens.
Example:
Hello → 15496
how → 703
are → 527
you → 345
These numbers are examples only.
3. What are tokens?
This is extremely important.
AI does not read words exactly like humans. It reads tokens.
A token can be:
- a word
- part of a word
- a symbol
- a punctuation mark
Example sentence:
I love programming
Could become:
"I"
" love"
" programming"
That is 3 tokens.
But:
unbelievable
May become:
un
believ
able
That is also 3 tokens.
Why tokens matter
AI pricing and limits are based on tokens.
Example:
Context window: 128,000 tokens
That means the model can process roughly that much text at once.
4. The learning process
The core idea is surprisingly simple:
Predict the next token.
Example input:
The sun rises in the
Model predicts:
east
Another example:
I am going to drink a cup of
Prediction:
coffee
During training:
- The model sees billions of examples.
- It makes predictions.
- If the prediction is wrong, it adjusts internal parameters.
- If the prediction is useful, it strengthens those patterns.
- This repeats billions of times.
This is called next-token prediction.
This single idea powers modern LLMs.
5. Then how does it answer questions?
You ask:
Explain Java inheritance
The model does not search a database of fixed answers.
Instead, it generates one token at a time.
Something like:
Explain
↓
Java
↓
inheritance
↓
is
↓
a
↓
concept
Each next token depends on the previous tokens.
Think of autocomplete on steroids.
Your phone keyboard:
I am going to...
May suggest:
home
ChatGPT does the same type of prediction, but at a massive scale.
6. Why does ChatGPT seem intelligent?
Because language contains knowledge.
Example:
To correctly explain:
How does a car engine work?
The model must understand relationships between:
Fuel
↓
Combustion
↓
Piston
↓
Movement
↓
Vehicle
During training, it learns these relationships.
It does not memorize every answer. It learns patterns.
7. Why does AI hallucinate?
This is very important for developers.
A database works like:
Question
|
Search exact record
|
Answer
An LLM works like:
Question
|
Predict likely answer
|
Generate response
The model's goal is:
Produce a convincing response.
Not:
Always produce a factually verified response.
Example:
"Who invented XYZ technology?"
If the model has weak information, it may generate a believable but incorrect answer.
That is hallucination.
8. Why AI needs RAG
This is where backend developers become important.
Suppose a company has:
10,000 internal documents
You don't want ChatGPT to guess.
You build RAG, which stands for Retrieval Augmented Generation.
Flow:
User Question
↓
Search Company Documents
↓
Find Relevant Information
↓
Send Information + Question to LLM
↓
Generate Answer
Now AI answers using your actual data.
Example:
"What is our leave policy?"
The AI application:
- Searches HR documents.
- Finds the policy.
- Answers based on that policy.
9. Important mental model
Think of ChatGPT like this:
User
|
↓
Your Prompt
|
↓
Tokenization
|
↓
LLM Model
|
↓
Next Token Prediction
|
↓
Generated Response
Backend developer connection
A normal backend:
Request
|
Controller
|
Service
|
Database
|
Response
AI backend:
Request
|
Controller
|
Prompt Builder
|
LLM API
|
Tools / Database / Vector DB
|
Response
The backend developer's job shifts from:
Writing every rule
to:
Designing systems around intelligent models.
Key takeaways
- LLM means Large Language Model.
- It learns from huge amounts of text.
- Text is converted into tokens.
- Training is mainly next-token prediction.
- ChatGPT does not search a database of answers.
- Hallucination happens because it predicts rather than verifies.
- RAG connects AI with real-world data.
Why AI hallucinates in long conversations
Yes, hallucination can increase in long conversations, but the reason is not that the AI gets tired.
1. LLMs have a context window
An LLM does not have infinite memory.
Every AI conversation is sent to the model as input:
System Instructions
+
Previous Conversation
+
Your New Question
|
↓
LLM
|
↓
Answer
The model only sees what fits inside its context window.
Example:
Context Window = 100,000 tokens
Your conversation:
Message 1 5,000 tokens
Message 2 10,000 tokens
Message 3 20,000 tokens
...
Message 50 90,000 tokens
Eventually, you hit the limit. Something has to happen.
2. Old information gets lost
When the conversation becomes too long, systems may:
- remove older messages
- summarize older messages
- compress information
Example:
You told the AI 50 messages ago:
"My project uses Spring Boot, PostgreSQL, Redis, and Kafka."
Later, the conversation becomes huge. The system summarizes:
"User is building a backend project."
The important details are lost.
Now you ask:
"How should I optimize Redis caching?"
The AI might answer generally because it forgot your exact setup.
3. Attention gets diluted
LLMs use something called attention.
The model tries to understand relationships between tokens.
Short conversation:
User:
I use Java Spring Boot.
Assistant:
Great, let's discuss Spring Boot.
Easy.
Long conversation:
500 messages
+
many topics
+
many instructions
+
many examples
Now the model has many things competing for attention.
Important information can become less prominent.
Think of reading a 500-page book and someone asks:
"What was the exact database choice mentioned on page 37?"
You may know the overall story but miss the small detail.
4. Conflicting information
Long chats can contain contradictions.
Example:
Message 1:
"I use MongoDB."
Message 100:
"I migrated to PostgreSQL."
Later:
"Write my database query."
The AI may get confused:
Should I write MongoDB syntax or SQL?
5. The AI does not have true memory
This is very important.
A normal program:
User user = database.findById(id);
The data is stored.
AI conversation:
Text → Model → Response
The model does not permanently remember. It only knows what is included in the current context.
How production AI systems solve this
1. Conversation summarization
Instead of keeping:
1000 messages
you create:
Summary:
User is building a Spring Boot application.
Uses PostgreSQL.
Needs help with AI integration.
Then send:
Summary + Recent Messages
2. Memory systems
AI agents often use databases.
Example:
"I prefer Java examples."
Store:
User Preferences Table
id: 123
preference: Java examples
Later:
User asks:
Explain API design
System retrieves:
User likes Java examples
AI responds accordingly.
3. RAG
For company knowledge:
All Documents
|
↓
Vector Database
|
↓
Retrieve relevant information
|
↓
LLM Answer
Real AI agent architecture
A production AI assistant usually looks like:
User
|
↓
AI Application
|
--------------------
| |
Conversation Memory Vector DB
| |
--------------------
|
↓
LLM
|
↓
Tools/APIs
The LLM is only one piece.
Simple analogy
Think of ChatGPT like a brilliant person sitting in an exam.
You give them:
- the question
- their notes
They answer.
But their desk has limited space.
If you put too many pages:
- some pages fall away
- some are summarized
- some details are missed
They do not become less intelligent. They just do not have access to all information.
Interview answer
One common question is:
Why do LLMs hallucinate more in long conversations?
Answer: LLMs operate within a fixed context window. As conversations grow, earlier information may be truncated, summarized, or receive less attention. This can cause loss of important context, contradictions, and more reliance on probabilistic generation, which increases hallucination.
This concept directly connects to Embeddings + Vector Databases, the technology that gives AI applications long-term memory.