AI Prerequisites for a Java Developer

🚀 AI Prerequisites for a Java Developer (LLM, RAG, Vector DB, LangChain4j)

Think of this as your AI foundation roadmap before building actual projects.

What is an LLM (Large Language Model)?
- LLM = a massive neural network trained on text, predicts the next token.
- Examples: OpenAI GPT-4, Google Gemini, Anthropic Claude.
- 🔑 Takeaway: Think “text prediction engine,” not “thinking machine.”
Tokens: The Unit of Text
- Input/output is in tokens, not words.
  Example: "Hello, world!" → [Hello, ,, " world", !].
- Why it matters:
  - Cost: API pricing depends on tokens.
  - Memory: Limited by model context window.
Prompt Engineering
- How you ask → determines what you get.
- Styles:
  - Zero-shot → just ask.
  - Few-shot → give examples in the prompt.
  - Chain-of-thought → encourage step-by-step reasoning.
- 🔑 Skill: Clarity, context, and examples = good outputs.

Embeddings: Text → Vectors
- Converts text into high-dimensional numbers (vector).
- Similar meaning = vectors close together.
- Analogy: “Paris” and “France” are closer than “Paris” and “Banana.”
Vector Search & Vector Databases
- Store embeddings in a vector DB (e.g., PGVector, Pinecone, Chroma).
- Retrieval = find the closest vectors to a query using cosine similarity / dot product.
- 🔑 Lets LLM “remember” and use external knowledge without retraining.
Context Window
- LLM memory limit measured in tokens (e.g., 32k).
- RAG solves this → retrieves only the most relevant chunks.
Chunking & Splitting
- Large docs must be split into smaller, semantically meaningful pieces (paragraphs, sections).
- Each chunk gets embedded separately.
Indexing
- Process of embedding + storing all chunks into a vector DB for later retrieval.

LLM APIs
- Interact with LLMs via REST APIs.
- Input = prompt JSON, output = model completion JSON.
- Need API key for authentication.
RAG Flow (High-Level)
- User Query → Embed → Search vector DB → Inject results into prompt → Call LLM → Return response.
Hallucination & Grounding
- Hallucination = when LLM makes stuff up.
- RAG reduces hallucinations by anchoring answers in your DB content.
Temperature
- Controls randomness.
  - Low (0–0.2) → deterministic, best for code.
  - High (0.7–1) → creative, best for writing.
Streaming
- Many LLMs support streaming (token-by-token response).
- Feels interactive, reduces latency.

Why Orchestration?
- Manually coding RAG steps = repetitive.
- Orchestration frameworks (LangChain, LangChain4j) provide prebuilt blocks.
LangChain4j Key Concepts
- Documents & Loaders → ingest text/code.
- Text Splitters → chunk documents.
- Embeddings → convert to vectors.
- Vector Stores → store embeddings (PGVector, Pinecone, etc.).
- Retrievers → query vector DB.
- Chains → pipelines connecting query → retrieval → LLM call.
- Agents → advanced flows where LLM chooses which tools/APIs to call.
Basic LangChain4j RAG Flow
- Input query → embed → retrieve top-N docs → add to LLM prompt → generate answer.

LLM (Large Language Model): Core AI engine (Gemini, GPT-4, Claude).
Prompt: Input instructions to an LLM.
Completion: Output text from an LLM.
Token: Smallest unit of text processed by LLMs.
Context Window: Max tokens an LLM can handle at once.
RAG (Retrieval-Augmented Generation): Technique combining LLM + external knowledge.
Embedding: Vector representation of text.
Vector Database: Optimized DB for vector similarity search.
Chunking/Splitting: Breaking large docs into smaller parts before embedding.
Indexing: Preprocessing + storing embeddings in a vector DB.
Retriever: Component that fetches most relevant chunks.
Hallucination: Incorrect output generated by an LLM.
Temperature: Controls randomness of output.
Orchestration: Managing multi-step flows (retrieval, LLM calls, tools).
LangChain4j: Java framework for LLM + RAG pipelines.