18 articles tagged with Rag.
A practitioner comparison of the RAG frameworks worth using in 2026, from LlamaIndex and LangChain to Haystack, DSPy, and raw code.
A demo RAG app is easy; one users trust is not. This is the map for reliable retrieval-augmented generation: grounding, evaluation, retrieval quality, guardrails, and safe rollout.
LangChain orchestrates agents and integrations, LlamaIndex owns retrieval and RAG. Here's where they overlap, where they don't, and which to reach for.
When RAG answers go sideways, the model usually isn't the problem. Here's the top-to-bottom checklist we run to find where retrieval actually breaks.
A million-token window doesn't retire your retrieval stack. Here's when to stuff the prompt, when to retrieve, and when to do both.
Prompt injection is not a prompt bug, it is an architecture problem. Here is how we design LLM apps so a poisoned document cannot hijack them.
A single answer-quality score hides where your RAG pipeline actually breaks. Split retrieval eval from generation eval and measure each one honestly.
A RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
The top model on the MTEB leaderboard made our search worse and our bill bigger. Here's how we actually picked an embedding model for a real RAG system.
Most agents that "need memory" actually need a smaller context window and a database. Here's how we cut a support agent's token bill by 60 percent by deleting memory.
Our RAG answers kept citing the wrong paragraph even when the right one was retrieved. A cross-encoder reranker fixed relevance but added 180ms. Here's when that trade pays off.
Pure vector search kept missing exact matches like error codes and CLI flags. Adding BM25 back and fusing the two lifted our retrieval recall by 11 points.
