a codebase, so it’s important to keep track of your prompt engineering work in a disciplined, structured way. More on this table format, the importance of tracking prompt engineering work, and the prompt selecting, parsing, ordering, ranking, or categorizing data try having your output returned in a structured format like JSON or XML. There are some benefits in returning JSON objects from a prompt that an example on how to return structured output. JSON Repair While returning data in JSON format offers numerous advantages, it's not without its drawbacks. The structured nature of JSON, while beneficial

operate until an exit condition is reached. Common exit conditions include tool calls, 
 a certain structured output, errors, or reaching a maximum number of turns. 14 A practical guide to building agents pattern, the same principles apply: keep components flexible, composable, and driven by clear, well-structured prompts. 17 A practical guide to building agents Manager pattern The manager pattern empowers agents, start with strong foundations: pair capable models with well-defined tools and clear, structured instructions. Use orchestration patterns that match your complexity level, starting with a single

on proprietary industry data. These platforms already have the workflows, the trust, and the structured data that AI thrives on. That gives them a head start in deploying domain-specific intelligence receive a plain-language fault diagnosis; a clinician can attach an X-ray to a note and get a structured report draft; and an analyst can combine charts, transcripts, and audio clips in a single query high school student to those of a PhD candidate. Professions centered on intaking large bodies of structured, historical data and outputting rules-based decisions and judgement, fall squarely in the core

com/mozilla/LPCNet) by ~40% - Bonus: Real-time on mobile CPUs for free 6 TVM specifics X78Structured and Unstructured Sparsity - Lots of 'free' wins from exploring sparsity in modern ML models

answer was to conduct intensive evals for every proposed application. An eval is simply a rigorous, structured process for measuring how AI models actually perform against benchmarks 
 in a given use case.