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llm-pro

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Integrate LLMs into apps: prompting, structured output, streaming, function calling, RAG, evals, cost and latency control. Use when adding language-model features to software.

skills/llm-pro/SKILL.md

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# LLM Pro

A practitioner's guide to shipping LLM features: prompt design, reliable structured output, streaming UX, tool/function calling, retrieval augmentation, evaluation, and keeping cost and latency under control. Vendor-neutral — the patterns apply to any chat/completions-style API.

## Overview

Most LLM integrations fail in the same ways: unvalidated free-text output parsed with regex, no handling for the model being confidently wrong, costs that scale with success, and prompts that silently break when the model is updated. The fix is engineering discipline: **schemas for output, evals for behavior, budgets for cost, and validation for everything.**

Think of the LLM as a brilliant but unreliable contractor: give precise specs (prompts + schemas), inspect the work (validation + evals), and never let it touch production data unsupervised.

## When to use

- Adding chat, summarization, extraction, classification, or generation features.
- Forcing machine-readable output (JSON) from a language model.
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