7 posts found
Most RAG demos look good, then fail quietly in production because content changes. This article shows how to build RAG that stays accurate when docs update daily, policies change, pages get removed, and multiple versions of truth exist.
Most LLM apps break in boring ways: missing fields, wrong types, partial JSON. This article shows a practical pattern to make structured outputs dependable with JSON Schema, validation, and repair loops.
Show how to turn a chatty LLM into a safe JSON-producing service that other systems can trust.
Show a practical way to move from guessing about LLM quality to measuring it using logs, labels, and simple experiments.
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.
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.
How to treat LLMs as strict, structured components instead of free-form text generators. Start with schemas and tools, then write prompts around them.