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.
Production AI work extends beyond choosing a model and writing a prompt. Teams need to decide how source material is prepared, retrieved, ranked, and presented with enough context for a useful answer. For retrieval-augmented generation, that can include chunking, keyword and vector search, result fusion, reranking, and citations. Each choice affects relevance, latency, cost, and how well the system handles questions that its data cannot answer.
Evaluation and safeguards matter just as much as retrieval. A repeatable test set helps compare changes to prompts, models, and ranking logic; deterministic validation can catch invalid generated structures before they reach users. The articles above explore these topics through practical implementation examples, including curriculum generation, employee assistants, and controlled tool calling. For a deeper look at evaluation, see the RAG evaluation framework and the production RAG pipeline walkthrough.