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
In April 2026, Google Labs released a spec called DESIGN.md. It's a design system specification readable by AI agents, packaged with a CLI validator: npx @google/design.md lint. With DESIGN.md in the picture, we now have three different file types for instructing AI agents. AGENTS.md has been spreading as an industry standard since 2025 (jointly developed by OpenAI, Google, Sourcegraph, Cursor, an
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 —