This post is a field guide packed with pro tips from the trenches of building real AI agents. You’ll learn how to structure tasks, keep agents focused, speed up execution, and keep your system stable as it grows.

Learn how RLHF works in the real world, starting with feedback collection and ending with model training, all explained in a clear and safe workflow.

Ship a production-ready, template-less invoice data extractor: jsonschema-validated JSON, retryable GPT-4o Vision, OCR fallback, and idempotent Postgres upserts with logs.

Build production-ready AI agents using nine actionable AI agent design principles for reliability, cost efficiency, observability, safety, portability, and scale.

Learn how AI agents actually work under the hood and when to use each one, and how to choose the right architecture for your goals.

Build a production-ready multi-document agent with LlamaIndex, turning PDFs into retrieval and summarization tools using semantic selection for accurate answers.

Achieve GDPR-ready GenAI retention: classify prompts, outputs, and logs, configure OpenAI/Azure settings, and automate lifecycle deletion with audits and reporting.

Upgrade your RAG pipeline with proven chunking, MMR, metadata filters, and BM25/TF‑IDF choices to deliver sharper, trustworthy answers consistently.

Learn how to design a clear instruction hierarchy to prevent rule overrides, policy leaks, and output drift in production-grade systems.

Build your first Knowledge Graph RAG system step-by-step: model entities, embed meaning, and retrieve grounded, context-rich answers using Neo4j.