RAG & Retrieval
Practical Lessons in RAG & Retrieval: Building Reliable AI Systems in Production
Real-world challenges and design decisions in implementing Retrieval-Augmented Generation for production AI systems
Thoughts on AI engineering, backend, frontend, and building modern software.
RAG & Retrieval
Real-world challenges and design decisions in implementing Retrieval-Augmented Generation for production AI systems
Embeddings & Vector Search
Building robust AI systems requires more than models—here’s how to design, optimize, and debug embeddings and vector search in real-world scenarios.
Data Engineering
Master real-world data engineering challenges with actionable insights from building scalable AI infrastructure. Learn how to avoid common pitfalls and build robust data pipelines.
Cloud & Infrastructure
Real-world challenges and design patterns for building scalable, cost-efficient, and production-ready AI systems
Backend Engineering
Mastering backend engineering for AI systems requires balancing performance, scalability, and maintainability. Learn real-world tradeoffs and design decisions from a senior engineer's experience.
AI Engineering
Learn real-world tradeoffs, failure modes, and design decisions from a senior engineer building AI systems at scale.
AI Agents
Learn real-world strategies for building reliable AI agents in production, including tradeoffs, failure modes, and design patterns from a senior engineer's perspective.