The first AI feature I shipped on a flat plan lost money on the third user who discovered it. Not slowly. Immediately. He was running a script through it on a loop because the UI did not stop him from doing that, and his single account burned through more in API costs that week than the feature was supposed to make in a month. I shipped the fix on a Sunday and rewrote the pricing on a Tuesday, and
A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —