From basics to agents in one structured playbook
This comprehensive guide turns prompt engineering into a repeatable system built on roles, structure, and explicit outputs—without requiring code. It moves from fundamentals to advanced techniques like chain-of-thought, self-consistency, and meta-prompting, then extends to agentic patterns such as ReAct, tree of thoughts, DSP, and self-reflection. The strategic goal is predictable, higher-quality results by controlling context, reasoning, and format end to end.
Points clés
- The post “Mastering Prompt Engineering (Complete 2026 Guide)” by Ivan Escribano was published on Nov 05, 2025 and is accessible for free on Substack.
- It frames every LLM exchange around three message types: system (behavior and limits), user (request), and assistant (responses/examples).
- The core five-step framework—what, who, how, input, output—standardizes objectives, roles, reasoning steps, structured inputs, and explicit formats.
- Recommended prompt structuring uses tagged formats (XML, JSON, Markdown) to delimit context, task, role, tone, and output constraints, reducing ambiguity.
- Advanced techniques covered include Chain-of-Thought (CoT), Least-to-Most, Self-Refine, Few-shot, Self-Consistency, and Meta-prompting.
- The guide extends prompting to agent design with ReAct, Tree of Thoughts (ToT), Dynamic Step Planning (DSP), and Self-Reflection loops.
- A ReAct example instructs tool use via the Brave Search API, coupling reasoning, actions, observations, and decisions in a closed loop.
- Models referenced include ChatGPT and Claude, with prompts tailored via roles, tone, and stepwise structures for consistency and quality.
- Metadata highlights: 2,772 words, cover image provided, tags include “Prompt engineering,” “AI Engineering,” “ChatGPT,” “LLM,” and “Artificial Intelligence”; author bio notes CTO @Weup with +500 users.
À retenir
Start by stealing the five-step skeleton (what/who/how/input/output) and you’ll stop throwing spaghetti at the model and start plating a meal. Use tags (yes, XML—embrace your inner librarian), add CoT when things get tricky, and iterate with Self-Refine like you mean it. And if you’re feeling fancy, wire up ReAct or ToT for agents—because nothing says “professional” like an AI that thinks before it acts… unlike most of us before coffee.
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