dspy-rag-pipeline

Retrieval-augmented generation (RAG) combines document retrieval with language model generation to produce factual, grounded answers. This Skill configures ColBERTv2 as the retriever, connects it to an LM within DSPy, defines a RAG module that retrieves passages then generates answers, and supports production evaluation and grounding checks.

Yes. This Skill assembles a complete RAG pipeline in DSPy where ColBERTv2 retrieves passages from a document corpus and the language model generates grounded responses. It enables joint optimization of both retrieval quality and answer generation simultaneously.

Standard language models generate answers from learned parameters alone, risking hallucination. RAG retrieves relevant passages from external documents first, then generates answers grounded in those passages, improving factuality and enabling access to current or domain-specific knowledge.

Configure ColBERTv2 as the retriever, set your language model, define the number of passages to retrieve, create a GenerateAnswer signature, and assemble a RAG module that chains retrieval then generation. The Skill provides production-quality patterns for this configuration.

Yes. This Skill includes production-ready evaluation patterns to assess accuracy and verify that generated answers are grounded in retrieved passages from your document corpus, supporting validation before deployment.

Joint optimization tunes the retriever and generator together rather than separately, ensuring retrieved passages support better answers and answers improve based on retrieval quality. DSPy enables this co-optimization within a single pipeline.