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Build production-ready RAG

Learn how to find the right information, give it to a language model, and show where the answer came from.

Start the Course

glob("docs/**/*.md")

grep("DEP-1042")

SQL: plan = 'Pro'

vector(question) → results

agent → choose tool → inspect

RAG finds outside information before a language model writes an answer.

I see most RAG tutorials begin with a vector database, which makes the two ideas feel inseparable. A vector database is one search tool, but RAG can retrieve information in other ways.

A coding agent can find files by name with glob, search inside them with grep, and read a small range of lines. A support system can read exact values from a database table. Embeddings can help when the question and file use different words with similar meaning.

Whatever method we use, the system needs to find supporting information, preserve where it came from, and give the model only what it can use.

flowchart LR
    Q[Question] --> D{Choose how to search}
    D --> G[File names and text]
    D --> S[Database lookup]
    D --> V[Similar meaning]
    G --> E[Supporting information]
    S --> E
    V --> E
    E --> A[Answer from the information]
    A --> C[Links to original files]

One project, built in layers

Foundations

Discover Sources, search their contents, inspect Evidence, and understand how coding agents retrieve context.

Build the pipeline

Turn files into searchable pieces, compare several search methods, generate cited answers, and test them against the same questions.

Agentic retrieval

Let a model choose read-only search tools, then compare your loop with a working open-source coding agent.

Production RAG

Protect private files, reuse expensive work safely, inspect slow or failed requests, and deploy the finished system.

What makes BuildRAG different

I teach the smallest version first, so you can see what each part does before adding another one. You will compare search methods against the same 40 test questions and read the failed examples instead of trusting one summary score.

I use focused libraries when they remove unrelated setup. I do not hide the search and ranking decisions inside a large RAG framework.

  • Python-first
  • Plain, visible search logic
  • Answers linked to Sources
  • Quality measured with test questions
  • Failures designed before deployment

Start with what RAG actually means