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.
Shipping Fast Without Breaking Things
How to maintain velocity in a startup without accumulating catastrophic technical debt — practical strategies from the trenches.
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.
Building an MVP That Actually Matters
Most MVPs fail not because of technology but because they solve problems nobody has. Here's how to build one that validates real demand.
Vector Databases Explained
What vector databases actually do, when you need one, and how they differ from traditional databases — explained without the hype.
Technical Debt as a Product Decision
Technical debt isn't always bad — it's a tool. The key is knowing when to take it on and when to pay it down, treated as a product decision.
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.
Validating Ideas Before Writing Code
The best code is code you never write. Learn frameworks for validating product ideas before investing engineering time.
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.