Tips, tutorials, and insights on prompt engineering for ChatGPT, Claude, Gemini, and more.
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A practical, prompt-engineering approach to generating on-brand, high-converting email campaigns with LLMs-without generic fluff.
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A practical prompt library for ATS-friendly, human-sounding resumes-plus a workflow that keeps the model from inventing experience.
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A practical way to make prompts readable, testable, and harder to break using XML-style sections plus Markdown fences.
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RAG and prompt engineering solve different failure modes. Here's how to choose, when to combine them, and what "good" looks like in production.
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A practical way to split messy requests into verifiable steps, reduce drift, and ship complex LLM features with less prompting drama.
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A practical, developer-friendly walkthrough of Tree-of-Thought prompting: how to branch, score, backtrack, and ship better reasoning.
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Self-consistency prompting samples multiple reasoning paths and votes on the final answer. Here's how it works, when it helps, and prompts you can steal.
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A practical, research-grounded way to have an LLM critique, rewrite, and regression-test your prompts-plus when meta prompting backfires.
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Role prompting isn't "act as an expert." It's a way to set scope, standards, and failure modes so the model reasons like a specialist.
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System prompts set the rules of the assistant; user prompts request the task. Here's how the two interact, fail, and how to use them well.
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Context engineering shifts the focus from clever wording to building the right context pipeline-memory, tools, retrieval, and constraints.
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A practical guide to choosing zero-shot or few-shot prompts, grounded in in-context learning research and real evaluation patterns.