mlane/llm-engineering-cheatsheet
Timeless principles and best practices for working with language models - tooling-agnostic, future-proof, and clear.
Covers universal prompting patterns (zero-shot, few-shot, role-based, constrained output) and the five-component prompt anatomy (role, task, input, constraints, examples) applicable across any model provider. Emphasizes treating LLMs as probabilistic next-token predictors rather than deterministic systems, with practical guidance on temperature/sampling controls, token budgeting, and systematic debugging via failure mode analysis. Includes a minimal Python example using OpenAI's API but remains framework-agnostic, grounded in principles that predate current tooling.
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Last pushed
Dec 24, 2025
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