Building with LLMs: A Practical Guide
A hands-on guide to integrating large language models into real applications — from API selection to prompt design to production pitfalls.
RAG Systems: Beyond the Basics
Go beyond simple retrieval-augmented generation — learn about chunking strategies, reranking, and evaluation metrics that make RAG systems actually useful.
Prompt Engineering Is Software Engineering
Stop treating prompts as throwaway text. Version them, test them, iterate on them — the same discipline you apply to code applies to prompts.
Vector Databases Explained
What vector databases actually do, when you need one, and how they differ from traditional databases — explained without the hype.
Fine-Tuning vs RAG: When to Use What
The most common question in AI engineering doesn't have a simple answer. Here's a framework for deciding between fine-tuning and RAG.
AI Agents: Architecture Patterns
From simple chains to autonomous agents — a practical guide to the architecture patterns behind AI agent systems that actually work in production.
Evals: The Missing Piece in AI Engineering
If you're not evaluating your AI outputs systematically, you're flying blind. Here's how to build an evaluation pipeline that catches regressions.