AI & History (Part 2): Designing against hallucination

Standard RAG systems are great at finding "similar" text, but they fail at historical reasoning. In this episode, we break down why semantic similarity isn't enough for accuracy and how to build a constrained reasoner that prioritizes truth over creativity.We explore the engineering architecture required to stop LLMs from hallucinating, moving from a passive database to an active system using agentic workflows and hybrid search.In this episode:• The RAG Trap: Why vector search retrieves thematically similar text but misses specific dates and context.Agent Sequencing: Replacing "autonomous" agents with strict guardrails, including date-first reasoning and triangulation strategies. Tools vs. Memory: Why we force the AI to use typed APIs (like get_personal_chunks) rather than relying on internal model state.Hybrid Search: Combining vector similarity with hard metadata filters to ensure precision.The Editor Layer: Implementing a final schema validation step where hallucinations are detected and rejected