Tool Use in Production: Function Calling Patterns That Actually Work
Most LLM agent failures trace back to under-specified tool schemas, not model capability. A practical guide to schema design, error handling, parallel calling, and security for production function calling.
Structured Outputs in Production: Engineering Reliable JSON from LLMs
A practical guide to getting schema-valid JSON from LLMs in production — covering constrained decoding, provider APIs, schema design pitfalls, and the validation patterns that keep agent chains from falling apart.
Reasoning Models in Production: When to Use Them and When Not To
A practical guide to deploying reasoning models in production — when the 5–10x cost premium is justified, how to build a routing architecture, and what metrics to track.
LLM-as-a-Judge: A Practical Guide to Building Evaluators That Actually Work
Most LLM evaluation setups are broken by design—wrong metrics, wrong people, wrong methodology. Here's a concrete framework for building LLM judges that actually correlate with quality and catch real regressions.
Common Pitfalls When Building Generative AI Applications
95% of generative AI pilots yield no measurable business impact. Here are eight engineering and product failures that kill AI projects — from problem selection through evaluation — with production examples.
Building Effective AI Agents: Patterns That Actually Work in Production
Most AI agent projects fail because engineers reach for complexity before they've earned it. A practical guide to the mental models, patterns, and operational principles that separate reliable agents from ones that hallucinate and loop.