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.