You are implementing multi-agent RAG for a legal discovery suite. A…

Question

You are implementing multi-agent RAG for a legal discovery suite. A Searcher agent retrieves passages; an Analyst agent reasons over them. Early tests show poor retrieval precision and lost clause boundaries. Which RAG design change best addresses embedding quality and retrieval precision?

Accepted Answer

Apply structure-aware chunking that respects clause/section boundaries, use high-quality domain-appropriate embeddings, and tune retrieval (top-k, filters, hybrid/semantic rank) with metadata for matter and privilege flags — Legal RAG lives or dies on chunk boundaries and retrieval precision. Keep clauses intact with structure-aware chunking, use solid embeddings, and tune hybrid/semantic retrieval with matter/privilege metadata. Searcher retrieves; Analyst reasons. Option A. Tiny 20-token shards (B) shred meaning. Titles-only (C) under-retrieves. Stuffing whole folders (D) is not multi-agent RAG and will not scale.

Answer

Chunk every document into fixed 20-token fragments with no overlap or metadata

Answer

Embed only document titles and never retrieve body text

Answer

Replace retrieval with stuffing entire matter folders into the Analyst prompt on every turn

You are implementing multi-agent RAG for a legal discovery suite. A Searcher agent retrieves passages; an Analyst agent reasons over them. Early tests show poor retrieval precision and lost clause boundaries. Which RAG design change best addresses embedding quality and retrieval precision?

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