DSPy Tutorial: Basic RAG

Source: https://dspy.ai/tutorials/rag/

This note captures the parts of the tutorial that map cleanly onto this repository's repository-grounded workflow.

Core Pattern

The tutorial demonstrates a compact DSPy RAG loop:

1. Configure a language model and a retriever.
2. Wrap retrieval and generation in a reusable DSPy module.
3. Evaluate against a task metric instead of relying on intuition.
4. Improve the program with a DSPy optimizer after the baseline is stable.

What Carries Over To This Repository

• Retrieval should remain an explicit, swappable step.
• Evaluation should use a small repository-specific question-answer set.
• Compiled or tuned artifacts should be saved and treated as versioned outputs.
• Optimization only makes sense after the baseline corpus and retrieval behavior are credible.

How The Current Scaffold Reflects That

• The package keeps retrieval, answer synthesis, utilities, and verification in separate modules.
• Example questions and training samples live in version control so notebooks, CLI runs, and tests share the same data.
• Azure deployment is treated as a downstream artifact step, not part of the baseline RAG execution path.

Next Steps Suggested By The Tutorial

• Replace the lexical overlap retriever with embeddings or an MCP-backed retrieval surface.
• Expand the repository-specific evaluation set.
• Add DSPy optimizer experiments only after the baseline evidence improves.