RAG Evaluation with Prometheus 2 in Haystack

ArticleDeepset Haystack TeamTutorial

A Deepset tutorial illustrates how to evaluate Haystack RAG pipelines using Prometheus 2 open-source models as alternatives to proprietary evaluators like GPT-4.

Key points

  1. Using closed models such as GPT-4 or Claude 3 Opus for evaluation introduces drawbacks regarding cost, data privacy, fairness, and predictability.
  2. Prometheus 2 is an open-source evaluation model family fine-tuned from Mistral-7B and Mixtral8x7B using open-source data.
  3. The models offer strong correlation with human judgement and proprietary systems for direct assessments and pairwise rankings.
  4. The guide demonstrates setting up an indexing and RAG pipeline using an InMemoryDocumentStore and a PubMedQA dataset as evaluation input.

Why it matters

Evaluating LLM outputs using proprietary APIs can be cost-prohibitive and violate data privacy standards. Prometheus 2 provides an open, customisable alternative that runs within local or self-hosted Haystack pipelines.

Analysis

Deepset focuses on bridging the gap between proprietary evaluation accuracy and open-source control. Prometheus 2 functions as a specialised LLM-as-a-judge, enabling reproducible quality assessments and custom rubrics within Haystack without third-party data exposure.

Found in

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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