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How to Improve RAG Quality: Chunking, Retrieval, and Reranking for Better Answers
A lot of teams build a RAG prototype, test it with a few documents, and feel good about the result. Then real users arrive, the questions get messier, the documents get longer, and the answers start drifting. That is usually the point where people realize RAG is not just about connecting a vector database to an…
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Retrieval-Augmented Generation (RAG): A Practical Developer Guide
If you’ve spent any time building with LLMs, you’ve probably run into the same problem sooner or later: the model sounds confident, but it doesn’t always know your data. It may answer beautifully and still be wrong. It may miss your latest policy update, your internal product names, or the one document that actually matters. That is…
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Building Trustworthy AI: The Thin AI, Thick Server Model
Building MCP-based AI systems that reason with models and execute through deterministic tools As engineers, we’re skeptical of systems that aren’t deterministic. Many current AI applications, which bundle reasoning and execution into a single model, are fundamentally unreliable for mission-critical tasks. A travel bot that hallucinates a visa requirement isn’t…