When Your Research Stack Implodes: Three Expensive Mistakes Devs Keep Repeating

On March 12, 2025, during a PDF-to-product-spec migration for a mid-size documentation engine, everything that could go wrong did. The pipeline produced plausible, beautifully written summaries - and they were wrong. Stakeholders used them to rewrite product requirements. Two sprints later, the new feature shipped with critical omissions that cost days of rework and a humbling postmortem. The moment felt like slow motion: the system sounded confident, the logs showed no exceptions, and the tests all passed. The root cause was an overreliance on surface-level search and a naive "bigger model = better research" assumption that hid brittle retrieval, citation errors, and unchecked hallucinations. This is the post-mortem you need before you build the next research-driven feature. I see this everywhere, and it's almost always wrong. The shiny object that starts the crash Teams treat conversational search like a panacea. The trap looks like this: "Let the LLM do the thinking - we'll fix the