In our last post, we perfected the search query. Now, we tackle a harder problem: teaching an AI to use it. This is the story of why our fully autonomous agent failed, and how we built a ‘Record and Refine’ workflow to make it truly powerful.

Building on our last discussion about RAG retrieval, this article tackles the query itself. We explore why a ‘one-size-fits-all’ boolean query fails and present a universal fallback strategy that ensures your AI can reliably search anywhere.

Discover the complexities of web search for RAG systems. This article explores common pitfalls, such as handling responsive design and e-commerce redirects, and provides robust solutions for reliable data retrieval.