RAG · LLM Evaluation · Applied AI
Production AI systems, not demos: hybrid retrieval and reranking, quantitative evaluation frameworks, and the deterministic validation that keeps AI-generated output trustworthy.
Featured Articles
Why "choose an LLM, write a prompt, ship it" works for demos—but what breaks when real users arrive. Production architecture, distributed systems, and SaaS engineering lessons.
Inside the process behind a production-grade RAG system: quantitative evaluation, hybrid retrieval, reranking, and on-prem data governance.
Part 1 of our RAG engineering series: hybrid retrieval, reciprocal rank fusion, cross-encoder reranking, and the roadmap that followed.
Why LLM-generated curricula need a deterministic validation graph, not a longer prompt: topological sort, cycle detection, and the repair-loop architecture behind our EdTech platform's course generator.
Why code should decide which tools to call, not LLMs. Deterministic tool calling vs agentic AI for money-critical workflows. Production refund system architecture.