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Prompting fundamentals

OpenAI’s article "Prompting fundamentals" outlines practical guidelines for crafting effective prompts to improve ChatGPT responses. It emphasizes concise prompts, specifying goals, audiences, and boundaries, and explains how to use plugins, connected sources, and Codex for more complex tasks. The piece also details workflow tips for follow‑up messages, scheduling, and cloud‑based Codex usage.

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The Core Development

Prompting is how you tell ChatGPT what you want to know, make, or change. A prompt can be a question, an instruction, or a goal. You don’t need technical syntax or a rigid formula. Start in your own words, review the response, and use follow-up messages to shape the result.

A short prompt is often enough. For larger or more important tasks, include the parts that matter:

Use only the parts that help. You don’t need to fill in every item or follow a required format.

Real-World Use Cases & Applications

Review the first result, refine the instructions, and reuse the workflow when it works.

Deployment & Availability

Start with the result, not a detailed list of steps. Include the audience or format when those details change what ChatGPT should produce.

Connected sources require the matching plugin, and availability can depend on your plan and workspace settings.

Strategic Perspective & Ecosystem Impact

This prompt explains what to create and who will read it. Describe a process when the process itself matters. Otherwise, leave ChatGPT room to search, compare information, and adjust its approach.

Share the information that could change the result. Add only the sources that matter, and explain what ChatGPT should take from each one.

When ChatGPT has access to connected sources, name where it should look and what it should find. You don’t need to describe every search it should run.

Why it matters

Clear prompting techniques directly impact the efficiency and reliability of AI interactions, enabling developers and users to harness AI tools more effectively and reduce time spent on iterative refinements.

Topic dossiers OpenAI