Implementing RAG with DSPy: Technical Guide

Source: https://medium.com/@arancibia.juan22/implementing-rag-with-dspy-a-technical-guide-a6ae15f6a455

This note distills the implementation ideas that matter for this repository's baseline design.

Core Stack In The Article

The article describes a document-centric RAG system that:

1. Load Markdown documents.
2. Split them into manageable sections.
3. Stores retrievable representations in a vector system.
4. Passes retrieved context into DSPy modules.
5. Compares prompt-only and RAG-based variants.
6. Evaluates answer quality with an explicit metric.
7. Optimizes the program with MIPROv2.

What Matters Here

• Repository files should be normalized before retrieval.
• Markdown and source files are a practical starting corpus for repository-grounded RAG.
• Evaluation needs reference answers, not just example questions.
• Optimization should target measured answer quality, not style.

How The Current Repo Maps To That

• src/repo_rag_lab/corpus.py and src/repo_rag_lab/retrieval.py provide the baseline load-chunk-rank flow.
• The tests and sample YAML files provide a starter evaluation surface.
• The current retriever is intentionally simple and serves as a baseline to replace later.
• Deployment concerns are kept separate from the baseline research loop.

Follow-On Work Implied By The Article

• Add embeddings or another stronger retrieval layer.
• Expand benchmark questions and expected answers.
• Compare direct file retrieval with MCP-powered repository services.