Vector retrieval
AssessTechniques
Semantic search over embedded representations to find relevant documents or tools.
Why it's here
Placed in Assess: 2 article(s) of evidence from 1 source(s), led by research-stage coverage, with 2 in the last 30 days. Confidence 30%.
Evidence (2)
- 6The New Stack·7/21/2026researchRetrieval engineering emerges as a bottleneck for AI apps
The article argues that as AI assistants, search, and agents become the main interface for many products, the quality of retrieval is increasingly central to user outcomes. It says retrieval engineering goes beyond vector search by orchestrating ranking, filtering, freshness, trust, and real-time updates to provide models with better context.
- 6The New Stack·7/18/2026researchAI Agents Need Better Context Layers, Not Just Smarter Models
The article argues that many AI agent reliability problems come from weak infrastructure around the model, not from the model itself. It highlights Karpathy’s workflow as an example of compiling raw knowledge into structured, queryable context, then using retrieval and tools to help agents operate more reliably.