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
We’ve spent a week building, where we predefined every single step. But what if you don't know the steps in advance? What if the AI needs to decide whether to search Google, check a database, or use a calculator based on the user's question? This is where we move from "Chains" to Agents. If a Chain is a fixed railroad track, an Agent is a self-driving car. It has a destination (your goal) and a se
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 —