Make Your AI Agents 10x Smarter with Hybrid Retrieval (n8n)

Explains why vector search alone is insufficient for reliable RAG (retrieval-augmented generation) agents. The video identifies nine question types where semantic similarity fails and demonstrates alternative retrieval strategies—metadata filtering, lexical/pattern matching, SQL and graph queries, multimodal retrieval, and precomputed APIs—to improve accuracy, reduce hallucinations, and design robust retrieval engineering for production AI agents.

– Describes the agent decision loop and why different questions demand different retrieval approaches; retrieval is treated as a tool call alongside APIs and actions, not a one-size-fits-all solution.

– Illustrates concrete failure modes for vector search: incomplete summaries, domain-specific identifiers, recency-dependent answers, tabular/aggregation queries, multihop reasoning, multimodal needs, and false premises.

– Presents alternative retrieval techniques: metadata filtering, lexical/pattern matching, hybrid search, SQL and graph queries, map-reduce or hierarchical summarization, multimodal embedding, precomputed API lookups, and verification/reranking for trustworthiness.

– Emphasizes engineering practices: model-aware retrieval design, access controls, evaluation against ground truth, and precomputing or delegating heavy calculations to improve reliability in production agents.

Quotes:

There’s a myth in the AI world that vector search is the silver bullet to ground your AI agents.

Similarity is not the same as relevance.

Some questions require processing the entire document — partial retrieval can be misleading.