Recall
Recall measures the proportion of relevant items successfully retrieved in information retrieval systems. It is calculated as relevant_retrieved / total_relevant.
Case Study: RAG Recall Enhancement
- Improvement: A client project increased RAG recall from 50-60% to >95% by leveraging large-language-models for advanced data indexing and structured query generation (see 2026 04 14 Improving RAG accuracy for retrieval).
- Initial Setup: Classic RAG application for internal customer service chatbot, retrieving data from multiple customer databases and document repositories.
- Key Techniques:
- LLM-powered data indexing (beyond simple vector search)
- Structured query generation to align with user intent
Source Notes
- 2026-04-23: Anthropic · ▶ source
- 2026-04-07: Chroma Context 1 Self Editing Search Agent for Efficient RAG · ▶ source
- 2026-04-10: Claude Managed Agents API Suite for Building and Deploying Autonomous · ▶ source
- 2026-04-24: Report: Top 10 Worst EVs to Avoid - Analysis of Performance and Value · ▶ source
- 2026-04-25: Claude Code · ▶ source