RAG Under the Hood: A Step-by-Step Breakdown for AI Engineers

Retrieval-Augmented Generation is the most important skill for AI engineers to have right now. Despite its reputation as a complex system, the core mechanics are straightforward. Here is exactly how RAG works under the hood, broken down into four steps.

Step 1: Chunk Your Documents

Start with your source material -- PDFs, webpages, Slack messages, whatever you want the AI to reference. Split these documents into chunks, but not randomly. Break them at semantic boundaries: paragraphs, sections, logical units of meaning. Leave a small amount of overlap between adjacent chunks so that when you pull one, you do not lose the surrounding context.

Step 2: Embed the Chunks

Run each chunk through an embedding model. This converts the text into a high-dimensional vector -- a list of numbers that represents the meaning of that chunk. The key property: two chunks about similar topics will produce vectors that are close together in the embedding space. This is what makes retrieval possible.

Step 3: Store in a Vector Database

Store all the resulting vectors in a purpose-built database. The database needs to support fast similarity search at scale.

Vector Database	Notes
Pinecone	Managed service, widely adopted
Weaviate	Open source, supports hybrid search
pgvector	PostgreSQL extension, good for teams already on Postgres

Step 4: Query and Retrieve

When a user asks a question, embed that question using the same embedding model. Then search your vector database for the closest matching vectors using cosine similarity. Pull the original text from those matching chunks and feed it into your large language model as context. The LLM uses that retrieved information to answer the question grounded in your actual data.

The Production Reality

The four steps above describe basic RAG. Production RAG is a different beast entirely -- it involves re-ranking, hybrid search, query decomposition, evaluation pipelines, and a host of other optimizations. For engineers wanting to go deeper, Doug Turnbull (who led search at Reddit and Shopify) offers lectures on advanced search and retrieval techniques.

Key Takeaways

RAG is a four-step process: chunk, embed, store, retrieve
Chunk at semantic boundaries with overlap to preserve context
Embedding models convert text into vectors that capture meaning
Vector databases enable fast similarity search across your document corpus
Basic RAG is simple; production RAG requires significant additional engineering
The retrieval step (the "R" in RAG) is often more impactful than prompt engineering

Resources

Doug Turnbull on Maven -- Search expert who led search at Reddit and Shopify
AI-Powered Search Course -- Deep dive on modern retrieval for humans and agents
Pinecone -- Managed vector database
Weaviate -- Open-source vector search engine
pgvector -- PostgreSQL vector similarity search extension