How to evaluate the quality of a Haystack search engine using RAGAS?

All answers to this question.

Evaluation is the most critical step often skipped. To integrate RAGAS with Haystack, you first need to run your pipeline over a "test set" of questions and store the questions, retrieved contexts, and generated answers. You then pass this data to the RagasEvaluator component. It will return metrics like "Faithfulness" (is the answer based on the context?) and "Answer Relevance." This allows you to objectively see if a change to your retriever (like switching from 5 to 10 chunks) actually helps or just adds noise. We use this in our CI/CD pipeline to ensure that no code update accidentally degrades the search quality.

   Answered 2025-09-22 by Barbara Wilson

Can I use a different, smaller LLM to do the evaluation to save money? Or does it have to be the same model that generated the answers?

   Answered 2025-09-25 by James Peterson

• James, you can definitely use a different model! In fact, it's often a best practice to use a "stronger" model for evaluation. For example, if you build a powerful AI search engine using Haystack that uses Llama 3 for the bot, you might use GPT-4 as the "evaluator." However, if you're on a budget, models like Prometheus or fine-tuned Mistral variants are specifically designed for "LLM-as-a-judge" tasks and can run locally, giving you a cost-effective way to get reliable quality scores.

     Commented 2025-09-27 by Thomas Baker

Don't forget to look at the "Context Precision" metric. It tells you if the most relevant documents are ranked at the top of your retrieval list.

   Answered 2025-09-30 by Mary Rodriguez

• Great point, Mary. High recall is useless if the right answer is buried at the bottom where the LLM might miss it due to "lost in the middle" effects.

     Commented 2025-10-01 by Christopher Evans