Structured Outputs with LLMs: How to Get Reliable JSON Every Time
Show how to turn a chatty LLM into a safe JSON-producing service that other systems can trust.
Category: LLM
5 posts found
Evaluating LLM Systems in Production: From Implicit Signals to Safe Experiments
Show a practical way to move from guessing about LLM quality to measuring it using logs, labels, and simple experiments.
Closing the Loop: Building Practical Feedback Loops for LLM Apps in Production
How to capture real user behavior and use it to quietly improve LLM prompts, tools, and policies over time. A practical guide to building feedback loops that turn messy usage into structured improvement.
Hard Boundaries for AI Agents: Time, Budget, and Permission Controls in Production
How to stop agents from running wild: clear limits on time, tokens, tools, and user data. Practical patterns to keep AI agents under control in production systems.
Schema-First LLM Apps: Designing JSON and Tool Calls Before Prompts
How to treat LLMs as strict, structured components instead of free-form text generators. Start with schemas and tools, then write prompts around them.