What You'll Build
By the end of this tutorial, you'll understand how to build a RAG (Retrieval-Augmented Generation) system — the architecture behind most production AI assistants in 2026. RAG lets you connect LLMs to your own data, eliminating hallucination and keeping responses grounded in facts.
Why RAG Matters
LLMs are powerful but have two critical weaknesses: they hallucinate when they don't know something, and their knowledge is frozen at training time. RAG solves both by retrieving relevant documents at query time and feeding them to the LLM as context.
Every major AI product — from ChatGPT's browsing mode to enterprise search — uses some form of RAG. Understanding it is essential for any AI engineer in 2026.
Step 1: Document Processing
The first step is preparing your documents for retrieval:
Loading
Ingest documents from any source — PDFs, web pages, databases, APIs. Use libraries like LangChain's document loaders or LlamaIndex for structured ingestion.
Chunking
Split documents into smaller pieces (typically 200-1000 tokens). Chunk size is critical: too small loses context, too large reduces retrieval precision. Recursive text splitting is the gold standard — it splits by paragraphs, then sentences, then characters.
Step 2: Creating Embeddings
Convert each text chunk into a dense vector (embedding) using a model like OpenAI's text-embedding-3 or open-source BGE. These vectors capture semantic meaning — "How to train a model" and "Steps for building ML" would have similar embeddings despite different words.
Store these vectors in a vector database: Chroma (easy start), Pinecone (production scale), pgvector (if you already use Postgres), or Qdrant (advanced filtering).
Step 3: Retrieval
When a user asks a question:
- Embed the query using the same model
- Search the vector database for the K most similar chunks
- Optionally re-rank results with a cross-encoder for better accuracy
Step 4: Generation
Combine the retrieved chunks with the user's question into a prompt:
"Based on the following context, answer the user's question. Context: [retrieved chunks]. Question: [user query]"
The LLM generates a response grounded in the retrieved information, dramatically reducing hallucination.
Advanced Techniques
- Hybrid search: Combine vector similarity with keyword search (BM25) for better coverage
- Query expansion: Use the LLM to generate multiple query variations
- HyDE: Generate a hypothetical answer first, then search with its embedding
- Agentic RAG: Multi-step retrieval where an agent breaks complex questions into sub-queries
Practice It
Our Build a Simple RAG Pipeline exercise lets you implement the core retrieval logic hands-on. Then dive deeper with the RAG Systems Deep Dive lesson for production-grade techniques.
Want to master the full LLM stack? Follow the LLM Engineer learning path — it covers transformers, prompt engineering, RAG, and production deployment.