RAG Evaluation Frameworks

The Retrieval Problem

Retrieval-Augmented Generation relies entirely on the retrieved data being accurate. If retrieval is bad, the generation is just a "hallucination in disguise." Improving your pipeline starts with how you store and query your data.

Measuring Success

Use metrics like "Retrieval Precision" (how many retrieved docs are relevant) and "Faithfulness" (does the model strictly answer from the retrieved docs?).

Improving Results

Experiment with "Query Expansion"—using an LLM to rewrite user questions into a more comprehensive search form before querying the vector store, drastically improving hit rates for vague queries.