Situation
The current search is agentic (LLM-driven). A RAG (vector search) approach may offer lower cost and/or acceptable accuracy. No empirical measurement has been done yet.
The input document provides a complete RAG-native design (stack, chunk design, metadata, indexing/query flows, verification plan) ready for implementation.
Pain
The team cannot decide whether to adopt RAG because there is no benchmark comparison between the current agentic search and a RAG implementation under the same E2E conditions.
Benefit
- Developers can make an evidence-based architecture decision (adopt or reject RAG)
- If adopted, users benefit from lower cost and/or faster responses
Input Documents
Success Criteria
🤖 Generated with Claude Code
Situation
The current search is agentic (LLM-driven). A RAG (vector search) approach may offer lower cost and/or acceptable accuracy. No empirical measurement has been done yet.
The input document provides a complete RAG-native design (stack, chunk design, metadata, indexing/query flows, verification plan) ready for implementation.
Pain
The team cannot decide whether to adopt RAG because there is no benchmark comparison between the current agentic search and a RAG implementation under the same E2E conditions.
Benefit
Input Documents
Success Criteria
🤖 Generated with Claude Code