Notes from building production AI systems.
Architecture, evals, observability, retrieval, and operating patterns for teams shipping agents and RAG systems.
How to Get Cited by AI Search in 2026
Learn how to get cited by AI search engines like ChatGPT, Perplexity, and Google AI Overviews. Structure content for answer extraction and earn citations.
Building Reliable RAG Systems
How to move a retrieval system from a promising demo to a production service that answers from the right context.
Preventing Hallucinations In AI Systems
Hallucinations are usually a systems problem. Fix the context, the decision boundaries, and the user experience before blaming the model.
Designing Production-Grade AI Agents
The jump from a chat demo to a reliable agent usually comes down to workflow control, tool design, and visible state.
Evaluation Methods For AI Systems
The right evaluation setup measures retrieval, generation, and business workflow outcomes separately so teams can improve the right layer.
Retrieval Vs Fine-Tuning
Retrieval and fine-tuning solve different problems. Choosing the right one depends on knowledge freshness, output behavior, and control needs.
AI Observability In Production
If you cannot inspect the context, the prompt, the tool calls, and the final output together, you do not really know how the system behaves.
Architecture Patterns For LLM Systems
Reliable LLM products usually converge on a few core patterns: a request layer, a context layer, an action layer, and a control plane around them.