AI Engineering
From Prototype to Production: Structuring AI Outputs as API Contracts
How to turn experimental prompts into reliable AI services with structured outputs and fallback strategies
Blog
Thoughts on AI engineering, backend, frontend, and building modern software.
Prompt Engineering
Prompt Contracts as API Contracts: Structuring AI Outputs for Production Reliability
How structured prompt contracts prevent ambiguity in AI systems, ensuring reliable and predictable outputs in production environments
AI Engineering
From Prototype to Production: Building Reliable AI Systems
Turn your AI prototype into a production-ready system with concrete strategies for contracts, fallbacks, and evaluation
RAG & Retrieval
Why Retrieval Quality Is the Hidden Weak Link in RAG Systems
Poor retrieval quality undermines RAG systems. Learn how chunking, metadata, and stale documents sabotage AI accuracy in production.
MLOps
Cost Spikes from Quiet Prompt Changes: Monitoring LLM Features in Production
After deploying an LLM feature, monitoring for cost spikes from subtle prompt changes is critical. Learn how to detect and mitigate these risks in production systems.
MLOps
Practical MLOps Lessons for Real-World AI Systems
Master MLOps challenges with real-world strategies and engineering judgment. Learn from my experiences building production AI systems.
LLMs
Practical Lessons in LLMs: Real-World Tradeoffs and Implementation Pitfalls
Building production systems with large language models requires balancing speed, cost, and reliability. Learn concrete strategies for deployment, optimization, and avoiding common pitfalls.
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
Embeddings & Vector Search
Practical Lessons in Embeddings & Vector Search for Production Systems
Building robust AI systems requires more than models—here’s how to design, optimize, and debug embeddings and vector search in real-world scenarios.
Cloud & Infrastructure
Practical Lessons in Cloud & Infrastructure for AI Engineers
Real-world challenges and design patterns for building scalable, cost-efficient, and production-ready AI systems