16 articles tagged with Rag.
Most teams shipping retrieval do not need a dedicated vector database. Here is where Postgres runs out, and which specialist actually helps when it does.
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.
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 RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
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.
Our support bot kept citing half a sentence and missing the answer that sat two lines below. The culprit wasn't the model, it was how we split the docs.
You can't improve retrieval you don't measure. The offline eval harness that lets us change embeddings, chunking, and rerankers with confidence instead of vibes — with the metrics that actually predict production quality.
They solve different problems. RAG injects knowledge; fine-tuning changes behavior. The decision criteria, the hybrid pattern, and what we'd do over.
