RAG Evals Flawed by Non-Deterministic Retrieval, Developer Finds

A developer encountered issues with their Retrieval-Augmented Generation (RAG) evaluation pipeline, initially suspecting the language model was degrading. However, upon closer inspection, the problem was traced to non-deterministic retrieval processes. Factors like tie-breaking in Approximate Nearest Neighbor (ANN) searches, parallel processing in hybrid search methods, and minor variations in chunking during data ingestion were found to cause inconsistent retrieval results. The proposed solution involves decoupling evaluation retrieval from production retrieval by implementing deterministic settings for evaluations, such as pinning specific parameters and using a fixed seed for ANN searches. AI

IMPACT Highlights a critical issue in evaluating RAG systems, potentially leading to more reliable performance metrics and better model tuning.

RANK_REASON Developer shares a technical post-mortem and solution for a common RAG evaluation problem.

Read on dev.to — LLM tag →

• BM25
• Hierarchical Navigable Small World graphs
• retrieval-augmented generation
• Scalable Nearest Neighbors

• product
• infra

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