Prompts¶
Platforms:
claudeopenaigeminim365-copilot
What Prompts Are¶
Prompts are the instructions you provide to an AI in natural language during a conversation. They're ephemeral, conversational, and reactive — you provide context and direction in the moment.
Every AI interaction starts with a prompt. It's the most fundamental building block — a well-crafted prompt is often all you need to get useful output, without any of the other blocks.
Key Characteristics¶
- Natural language — you write prompts the way you'd explain a task to a knowledgeable colleague
- Ephemeral — used in the moment, within a single conversation turn
- Reactive — you adjust and refine based on the AI's response
- Range of complexity — from a single sentence to a detailed multi-section instruction with role, task, context, and format guidance
When to Use a Prompt Alone¶
A prompt by itself is sufficient when:
- The task is a one-off (no need to repeat it)
- The AI's training data has everything it needs (no specialized knowledge required)
- The output format is simple or you can describe it inline
- You don't need persistence across conversations
When you find yourself writing the same prompt repeatedly or needing to attach the same context every time, that's a signal to consider other building blocks — Context, Projects, or Skills.
Anatomy of an Effective Prompt¶
The best prompts address four elements:
| Element | What It Covers | Example |
|---|---|---|
| Role | Who the AI should be — expertise, perspective, personality | "You are a senior copywriter specializing in B2B SaaS" |
| Task | What the AI should do — the specific action or output | "Write three subject line options for this product launch email" |
| Context | Background the AI needs — constraints, audience, standards | "Our audience is enterprise CTOs. Tone should be authoritative, not salesy" |
| Format | How the output should be structured | "Present each option with a subject line and a one-sentence rationale" |
Not every prompt needs all four elements. A simple question needs only the task. But as complexity grows, adding role, context, and format dramatically improves output quality.
Platform Implementations¶
| Platform | How Prompts Work |
|---|---|
| Claude | Messages in conversation, system prompts, project instructions |
| OpenAI (ChatGPT) | Messages in conversation, system prompts, Custom GPT instructions |
| Gemini | Messages in conversation, Gem instructions |
| M365 Copilot | Chat messages, prompts within Copilot agents |
Common Prompt Anti-Patterns¶
Vague instructions — "Help me with marketing" gives the AI nothing to anchor on. Be specific: "Draft a 200-word LinkedIn post announcing our Q3 product update, targeting engineering managers."
Overloading a single prompt — Asking the AI to research, analyze, write, format, and review in one prompt leads to shallow results. Break complex work into sequential prompts or use a Skill.
Ignoring format guidance — If you don't specify output structure, you get whatever the model defaults to. State what you want: bullet points, a table, a specific word count, or a particular template.
Repeating yourself every conversation — If you're pasting the same preamble into every chat, you need a Project with custom instructions instead.
Guides¶
| Guide | Description |
|---|---|
| Prompt Engineering | Core techniques — context windows, system prompts, few-shot learning, chain-of-thought |
| Project Instructions | When your prompts evolve into standing instructions for a project workspace |
Related¶
- Context Engineering — the broader discipline that prompt engineering is part of
- Agentic Building Blocks — Prompts in the context of all seven building blocks
- AI Use Cases — see how prompts are used across content creation, research, coding, data analysis, ideation, and automation
- Projects — where prompts become persistent custom instructions
- Patterns — reusable prompt structures