https://nivamind.com/insights Practical writing on building AI systems that hold up in production. en https://nivamind.com/insights/why-most-rag-systems-fail-in-production https://nivamind.com/insights/why-most-rag-systems-fail-in-production Retrieval-augmented generation demos are easy and production RAG is not. The failures are predictable, and almost all of them happen in retrieval rather than generation. Tue, 11 Aug 2026 09:00:00 GMT https://nivamind.com/insights/evaluating-llm-features-without-fooling-yourself https://nivamind.com/insights/evaluating-llm-features-without-fooling-yourself A practical evaluation stack: golden sets, LLM-as-judge with its known biases, regression gates in CI, and the metrics that actually correlate with user trust. Tue, 28 Jul 2026 09:00:00 GMT https://nivamind.com/insights/the-real-cost-of-an-ai-feature https://nivamind.com/insights/the-real-cost-of-an-ai-feature Token pricing is the smallest line in the budget. Here is the cost model we use with clients, covering inference, retrieval infrastructure, evaluation, human review and the ongoing maintenance nobody scopes. Tue, 14 Jul 2026 09:00:00 GMT