A few months ago, if you had asked me whether Structured Outputs solved hallucinations, I probably...
Structured Outputs solve format validation, not semantic accuracy—a model can return valid JSON containing hallucinated values. Validation now requires two layers: structural (format-checked via schema) and semantic (evidence, confidence, attribution). ShapeCraft distinguishes guarantee levels (native/constrained/best-effort); the production frontier shifts from fixing JSON to ensuring extracted data trustworthiness.
A few months ago, if you had asked me whether Structured Outputs solved hallucinations, I probably would have said yes.
Today, I wouldn't.
Not because Structured Outputs don't work-they absolutely do.
But because they solve a different problem.
After building ShapeCraft, an open-source structured output library supporting OpenAI, Groq, Ollama (GBNF), and Anthropic, I realized something that fundamentally changed how I think about production AI.
Your LLM Can Return Perfect JSON and Still Be Completely Wrong Most developers (including...
Stop AI coding agent hallucinations with a Node.js AI agent codebase semantic RAG that uses AST parsing and symbol graphs. I cut…
Here's a claim that might sound extreme at first: AI hallucinations will never be fully solved by...
Hallucination isn't a model bug you're stuck with. It's almost always a context design problem — and it has a fix.
Most engineers spot AI coding agent hallucinations but don't fix the underlying cause. How I debug context, rules files, and…
Hallucination isn't a bug in LLMs — it's the mechanism. The real problem is misallocating non-determinism, and why "SKILLS.md is…
