Our vector search accuracy is terrible on legal documents and I need immediate advice on whether switching to Cohere rerankers will actually solve this problem today.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Our vector search accuracy is terrible on legal documents and I need immediate advice on whether switching to Cohere rerankers will actually solve this problem today.
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
Cohere Rerank latest documentation rerank model multilingual v4 legal document retrieval 2026 Google ↗
Cohere rerankers legal document search benchmark reranking limitations candidate retrieval recall chunking Google ↗
- Cohere Rerank · docs.cohere.com
- Release Notes · docs.cohere.com
- architecture-center/docs/ai-ml/guide/rag/rag-information-retrieval.md at main · MicrosoftDocs/architecture-center · github.com
- Master Reranking with Cohere Models · docs.cohere.com
- Best Practices for using Rerank · docs.cohere.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Cohere Rerank latest documentation rerank model multilingual v4 legal document retrieval 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
Cohere rerankers legal document search benchmark reranking limitations candidate retrieval recall chunking OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |