Once You Know This, Building RAG Agents Becomes Easy in n8n

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In this video, I break down the different ways you can handle retrieval and context in RAG systems when building AI agents in n8n. I start by explaining why chunk-based retrieval often causes hallucinations and inaccurate answers, especially when the agent is missing full context.

Then I walk through three practical approaches I actually use in real systems: using filters to narrow context, using SQL queries to pass full and structured context to the agent, and using vector search when semantic matching makes sense. For each approach, I explain what it is, how it works, when it breaks down, and when it is the right tool for the job.

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TIMESTAMPS
00:00 What We’re Covering
01:00 The Problem with Chunk Based Retrieval
03:47 1) Filters
07:47 2) SQL Query
11:20 3) Full Context
15:40 4) Vector Search
17:00 Want to Master AI Automations?