Understanding Large Language Models (LLMs) for Java Developers
“Before building AI applications, we must first understand what exactly is happening inside these magical systems.”
Welcome to the Journey
In the previous article, we discussed the roadmap from Enterprise Architect to AI Engineer.
Now the real journey begins.
As Java developers, we have spent years learning:
- JVM internals
- Garbage Collection
- Thread pools
- Database indexing
- Caching
- Distributed systems
AI also has its own fundamentals.
Before learning RAG, LangChain, Spring AI, or Agents, we must understand:
What exactly is a Large Language Model?
What is an LLM?
LLM stands for:
Large Language Model
Let’s break this down.
Large
Trained on massive amounts of data.
- Books
- Websites
- Research papers
- Documentation
- Code repositories
Measured in:
- Billions of parameters
- Trillions of tokens
Language
Understands and generates human language.
Examples:
- English
- Java code
- SQL
- JSON
- XML
- Documentation
Interestingly, code is also considered a language.
Model
A mathematical representation trained to predict:
What comes next?
That is the core principle.
The Next Word Prediction Machine
Consider:
Java is a programming ______
Most humans answer:
language
The model does the same.
It predicts the next token.
For example:
Java is a programming language.
Prediction happens repeatedly:
Java
Java is
Java is a
Java is a programming
Java is a programming language
Millions of these predictions produce coherent responses.
Why LLMs Feel Intelligent
They are surprisingly good at:
- Pattern recognition
- Knowledge retrieval
- Language generation
- Reasoning approximations
- Summarization
- Translation
But internally:
They are sophisticated prediction engines.
Understanding Tokens
LLMs do not see words.
They see tokens.
Example:
Spring Boot is awesome.
May become:
Spring
Boot
is
awesome
.
Or:
Spr
ing
Boot
depending upon tokenizer implementation.
Why Tokens Matter
Models have limits.
Examples:
| Model | Context Window |
|---|---|
| GPT-3.5 | 16K |
| GPT-4 | 128K |
| Claude | 200K+ |
This means:
- Input
- Instructions
- Documents
- Response
must all fit into the context window.
Example
Suppose:
- User question = 200 tokens
- Documents = 8,000 tokens
- Instructions = 500 tokens
- Response = 1,000 tokens
Total:
9700 tokens
This becomes important later in RAG.
Parameters: The Brain of an LLM
A parameter is similar to a weight.
Examples:
- 7 billion parameters
- 70 billion parameters
- 175 billion parameters
More parameters generally mean:
- Better reasoning
- Better language understanding
- More knowledge
But also:
- More memory
- More GPUs
- Higher costs
Training an LLM
Training involves:
Internet Data
↓
Tokenizer
↓
Neural Network
↓
Prediction
↓
Error Calculation
↓
Weight Adjustment
This process repeats trillions of times.
Training vs Inference
Training
Very expensive.
Requires:
- Thousands of GPUs
- Weeks or months
- Massive datasets
Only companies like:
- OpenAI
- Anthropic
- Meta
typically perform this.
Inference
Using the trained model.
When you ask:
Explain Spring Boot.
The model performs inference.
Most AI applications only use inference.
What is a Transformer?
Before transformers:
- RNN
- LSTM
had limitations.
The breakthrough paper:
Attention Is All You Need
introduced:
Transformer Architecture
This changed everything.
Self-Attention
Suppose:
Rahul submitted his code because he completed the task.
What does “he” refer to?
The model pays attention to:
- Rahul
- submitted
- completed
This mechanism is called:
Attention
Why Transformers Won
Advantages:
- Parallel processing
- Better context understanding
- Long-distance relationships
- Better scalability
Today:
- GPT
- Claude
- Gemini
- Llama
all use transformer architectures.
The Context Window
Imagine a whiteboard.
The model can only see what is written on it.
Example:
System Prompt
User Question
Documents
Conversation History
Once the whiteboard becomes full:
Older information disappears.
Why This Matters
Suppose:
Upload 500-page PDF.
Ask question.
The model cannot read all 500 pages directly.
This problem leads us toward:
Retrieval Augmented Generation (RAG)
Temperature
Temperature controls creativity.
| Temperature | Behavior |
|---|---|
| 0 | Deterministic |
| 0.2 | Stable |
| 0.5 | Balanced |
| 1.0 | Creative |
| 1.5 | Highly random |
Example
Question:
Name a programming language.
Temperature 0:
Java
Temperature 1.2:
Java, Rust, Kotlin, Elixir...
Hallucinations
One important truth:
LLMs can be confidently wrong.
Example:
Spring Boot 8 was released in 2023.
The model may invent facts.
Why?
Because it predicts plausible text.
Not truth.
Causes of Hallucination
- Missing knowledge
- Insufficient context
- Ambiguous questions
- Poor prompts
How Enterprises Reduce Hallucinations
- RAG
- Citations
- Knowledge bases
- Human validation
- Guardrails
Fine Tuning
Fine tuning means:
Teaching a model specialized knowledge.
Examples:
- Medical AI
- Banking AI
- Legal AI
However:
Most enterprise systems today use:
- Prompt engineering
- RAG
instead of expensive fine tuning.
RLHF
RLHF:
Reinforcement Learning from Human Feedback
Humans rank responses:
Response A ✓
Response B ✗
The model learns preferred behavior.
This improves:
- Safety
- Helpfulness
- Alignment
LLM Request Lifecycle
User Question
↓
Tokenizer
↓
Context Assembly
↓
Transformer Layers
↓
Attention Mechanisms
↓
Token Prediction
↓
Response Generation
Java Analogy
| Java | AI |
|---|---|
| JVM | LLM Runtime |
| Bytecode | Tokens |
| Heap | Context Window |
| GC | Context Management |
| Thread Pool | GPU Parallelism |
| APIs | Prompts |
| Cache | Vector Database |
This analogy helps Java developers understand AI concepts faster.
Practical Exercise 1
Ask an LLM:
Explain Spring Boot.
Now ask:
Explain Spring Boot to a Java architect with microservices experience.
Observe the difference.
Practical Exercise 2
Ask:
What is Java?
Then ask:
Explain Java in 50 words.
Observe:
- Context changes output.
- Instructions shape responses.
Practical Exercise 3
Experiment with temperature.
Ask:
Write a poem about Java.
Try:
- Temperature 0.2
- Temperature 1.0
Observe creativity differences.
Key Takeaways
✔ LLMs predict the next token.
✔ Tokens are the actual language of models.
✔ Context windows are limited.
✔ Transformers introduced attention.
✔ Hallucinations are normal.
✔ Prompts matter.
✔ RAG solves knowledge problems.
✔ Most enterprises use inference, not training.
Interview Questions
1. What is an LLM?
A model trained to predict the next token.
2. What is a token?
The smallest unit processed by the model.
3. What causes hallucinations?
Lack of context or missing knowledge.
4. What is temperature?
Controls randomness.
5. What is a context window?
The amount of information the model can process simultaneously.
What’s Next?
In the next article:
Part 2 — Tokens, Embeddings, and Vector Mathematics for Java Developers
We will learn:
- Why text becomes vectors.
- Cosine similarity.
- Semantic search.
- Embeddings.
- Why vector databases exist.
- The foundation of RAG systems.
Because once we understand embeddings, we can finally begin building our first enterprise AI application.
“Understanding LLMs is like understanding the JVM before becoming a Java architect. Everything else builds on this foundation.”
