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How to build a RAG question-answering prototype with a golden set with AI Engineer Notebooks

Build retrieval-augmented answers from raw API calls, then score them against a golden set.

Authorization notice. AI Engineer Notebooks is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA backend engineer assigned a first document Q&A feature who needs to show it works before it ships.

The problemA plain model call invents answers about your documents, and a demo that looks right often fails on real questions.

The outcomeA working retrieve, augment, generate loop with a golden set of questions and a measured pass rate.

What you need first

  • A Google account to open Colab notebooks
  • A free Groq API key from console.groq.com (no credit card needed)
  • The key saved as a Colab secret, as the setup notebook describes
  • Basic Python and comfort reading API responses

Steps

  1. Open the setup notebook at 00-setup/00-environment.ipynb from the Colab badge and add your Groq key as a Colab secret.
  2. Run 03-rag/00-what-is-rag.ipynb to see the retrieve, augment, generate loop in about 15 lines.
  3. Work through 03-rag/01-embeddings-retrieval.ipynb until retrieval returns the right passages for your questions.
  4. Open 04-evals/01-golden-sets.ipynb and build a golden set of questions with expected answers for your RAG system.
  5. Run 03-rag/04-why-rag-fails.ipynb on any bad answers to find out whether retrieval or generation is the bottleneck.

Prompt for your agent

Using the repo calmrocks/ai-engineer-notebooks, work through 03-rag/00-what-is-rag.ipynb and 04-evals/01-golden-sets.ipynb against the documents in <placeholder folder>. Report the golden-set pass rate and list the three questions that failed. Verify that each failed answer is checked against the retrieved passages, not only the final text.

Caveat. The notebooks use the Groq API on Colab, so you will need your own free Groq key and a Google account before the first run.

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