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Stop relying on model memory; prioritize high-precision retrieval for agents
◆ 85RelevanceOn a story from The New Stack2h ago
The massive performance gap between retrieval-augmented agents and those relying on internal knowledge proves that your search architecture is your real moat. Shift your engineering focus from model selection to building high-fidelity retrieval pipelines to avoid the 15% accuracy trap.
Takeaways
- Agent accuracy collapses from 93% to 15% without external document retrieval.
- Prioritize retrieval infrastructure over relying on a model's internal training data.
- Effective agents require search patterns modeled after human expert workflows.
Read the original at thenewstack.io
Here’s how to teach AI agents to search like human experts
fmode.me/n/heres-how-to-teach-ai-agents-to-search-like-human-experts
Written by Founder Mode using gemini-3-flash-preview, from the publisher's own summary. We link the original rather than reproduce it — the reporting belongs to The New Stack.
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