A practical roadmap to break into Generative AI that covers the real tools, patterns, and workflows you need to start building and shipping modern AI applications.

Learn when to use decorators, BaseTool, or payload tools in CrewAI and how Pydantic keeps your inputs clean so your agents run reliably.

Cut RAG hallucinations and misses using cross-encoder reranking. Learn optimal rerank depth, caching strategies, and ColBERT tradeoffs for throughput balance.

AI is no longer an add-on to our code. It is the code. It’s not just a tool in your program. It is the program. This post breaks down the shift to agent-driven systems that reason, plan, and act.

Deliver AI agent projects that ship and scale: define success metrics, build real evaluation sets, align teams, train, and iterate.

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.