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 Q3 2024, 72% of production RAG pipelines failed to meet p99 latency SLAs for multimodal queries, according to a Datadog survey of 1,200 engineering teams. Most blamed fragmented toolchains for text and image retrieval—until Stable Diffusion 3.0’s embedding API and Llama 4’s 1M-token context window changed the game. This is the definitive guide to building unified multimodal RAG pipelines that c
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 —