How To Write AI Prompts In 2026

Davis Christenhuis

-February 20, 2026

You ask your AI assistant a question. The response comes back generic, vague, or completely missing the point. You rephrase and try again. Same problem. The issue isn't the AI. It's how you're prompting it.

Knowing how to write AI prompts changes everything. Most people approach AI like a search engine—type a few keywords and expect useful output. But AI models work differently. They need context, structure, and clarity to generate results worth using.

📌 TL;DR

In a rush? Here's how to write AI prompts that work:

Be specific about what you want: define scope, format, and desired output clearly
Provide context: explain who it's for, what problem you're solving, and any relevant background
Assign a role: tell the AI what perspective to take (teacher, technical writer, sales coach)
Specify the output format: request bullet points, tables, or specific structures
Use examples: show the AI what good output looks like
Break down complex tasks: guide the AI through multi-step requests one piece at a time
Set constraints: tell the AI what not to do (tone, length, content boundaries)

What is an AI prompt?

An AI prompt is the instruction you give an AI model to generate a specific output. It can be one sentence: "Write a job posting for a product designer." Or it can be detailed: multiple paragraphs explaining the role, the company, the tone you want, and the format you need.

The difference between prompts and search queries matters. When you search Google, you're looking for existing content that matches your keywords. When you prompt an AI, you're asking it to create something new based on patterns it learned from training data.

This means you need to give it more information. The model doesn't know your business, your audience, or what you actually want unless you say so.

One-time prompts vs. standing instructions

There are two ways people use prompts:

One-time prompts are single questions. You ask, the AI answers, you move on. This works fine for quick tasks.
Standing instructions are reusable templates. You write them once, and the AI applies them every time it runs that task. Instead of re-explaining your requirements each time, you set the rules upfront.

This second approach matters for business work. If you're summarizing meeting notes every week or drafting customer emails daily, you want consistency.

💡 Curious about turning prompts into agents? Discover Dust →

How to write AI prompts

Writing good prompts isn't hard. It just requires intentional structure. Here's what works.

Be specific about what you want

Vague requests produce vague results.

"Write about customer service" could mean anything. A blog post? An email? A training guide? For what audience? How long?
"Write a 500-word blog post explaining how AI cuts customer service response times, with two real examples from B2B SaaS companies" gives the model a clear target.

The more precise you are about scope, format, and content, the closer the output will be to what you actually need.

Provide context

AI doesn't know your situation. It doesn't know your industry, your audience, or the problem you're trying to solve.

"Write an email to a customer" leaves too much open. Is this a sales email? Support? An apology? Who's the customer? What happened?
"Write a professional email to a B2B customer who was overcharged on their last invoice. Apologize for the error, confirm we've corrected it, and let them know the refund will appear in 3 business days" gives the model what it needs to write something useful.

Context prevents the AI from guessing wrong.

Assign a role

Telling the AI to act as someone specific often improves results.

"Act as a technical writer" or "You are a sales coach" shifts how the model thinks about the task. It adjusts tone, word choice, and approach.
"Explain how APIs work" might get you a Wikipedia-style definition.
"Act as a high school teacher explaining APIs to students with no coding experience. Use analogies and skip the jargon" gets you something clearer and more useful for that audience.

Role framing helps the model understand not just what to write, but how to write it.

Specify the output format

If you need bullet points, say so. If you want a table, ask for one. If you need JSON, specify that.

AI can generate outputs in many different structures, but it won't know which one you want unless you tell it.

"Compare these three tools" might get you three paragraphs of prose.
"Compare these three tools in a table with columns for Features, Pricing, and Best Use Case" gets you something you can actually use without reformatting.

Use examples

Showing the AI what you want works better than describing it.

"Write a product headline" could go in any direction.
"Write a product headline in this style: 'Turn conversations into customers with AI-powered chat' or 'Close deals faster with intelligent sales automation'" gives the model a template.

Examples set clear expectations for tone, structure, and style.

Break down complex tasks

For multi-step requests, guide the AI through each piece.

"Analyze this sales data and recommend improvements" is too broad.
"Analyze this sales data by doing the following: 1) Identify the top 3 revenue sources, 2) Calculate month-over-month growth for each, 3) Highlight any declining trends, 4) Recommend two actions to improve performance" walks the model through the task step by step.

This approach reduces confusion and improves accuracy.

Set constraints

Tell the AI what not to do. Constraints prevent outputs you don't want and keep the model focused.

Common constraints:

Tone boundaries: "Stay professional, no humor"
Length limits: "Keep under 200 words"
Content rules: "Only use facts from the provided documents, no speculation"
Format restrictions: "Reply in bullet points only"

Constraints help you avoid irrelevant, off-brand, or overly long responses.

How Dust makes prompting easier

Writing prompts gets faster with practice. But it's even easier when you have structure and feedback built in. Dust gives you both. Instead of guessing what makes a good prompt, you get frameworks and real-time guidance as you build.

With Dust, you build AI agents that work with your actual company data. You're not just writing one-off prompts in a chat window. You're creating reusable agents that connect to your knowledge bases, tools, and team workflows.

Structured frameworks for agent instructions

When you build an agent in Dust, you can follow a recommended prompt framework designed around what actually works:

Define the agent's role and goal
Provide relevant context
Add step-by-step instructions
Set constraints
Specify output format

This structure makes sure you don't skip critical pieces when building agents for recurring tasks.

💡 Want to see this in action? Learn how to build an AI agent from scratch →

Drafting your agent instructions with @PromptWriter

Not sure how to write your agent instructions from scratch? Dust includes @PromptWriter, an agent template from the Template Library designed specifically to help you draft clear, structured instructions for other agents.

Describe the task your agent needs to perform, and @PromptWriter will generate a full set of instructions — covering role, context, steps, constraints, and output format — so you're not starting from a blank page.

Debugging your agent's reasoning

One of the hardest parts of prompt work is figuring out why the AI gave a certain answer.

Dust lets you inspect every step your agent took through a detailed activity panel: which knowledge sources it searched, what information it retrieved, which tools it used, and in what order.

This transparency makes it easy to spot where your instructions need adjustment.

Built for real workflows

Dust agents aren't isolated chatbots. They connect to your team's actual knowledge sources—Google Drive, Notion, Slack, Zendesk, HubSpot—and work across platforms (web, Slack, Chrome extension).

Your prompts don't just generate text. They trigger actions, search company data, and deliver answers grounded in your specific context.

Dust turns prompt engineering into a practical system for business teams who need reliable AI, not just experiments.

💡 Want to make your AI prompts more effective? Try Dust free for 14 days to build agents that can turn your prompts into workflows →

Examples of AI prompts

Here's what the difference between weak and strong prompts looks like in practice for marketing, sales, and engineering teams.

Example 1: Marketing

Weak prompt: "Write a blog post about our product"

Strong prompt: "Write a 600-word blog post explaining how AI agents help marketing teams create content faster. Target audience: B2B marketing managers at mid-size companies. Include: 1) Common content bottlenecks they face, 2) Three specific ways AI speeds up content creation, 3) One real-world example. Tone: professional but conversational. Use short paragraphs and include subheadings."

Why it works: Specifies length, audience, structure, key points to cover, and desired tone.

Example 2: Sales

Weak prompt: "Help me prepare for a sales call"

Strong prompt: "I have a sales call tomorrow with a VP of Sales at a 200-person B2B SaaS company. They're struggling with long sales cycles and inconsistent follow-up. Create a call prep brief that includes: 1) Three discovery questions about their current sales process, 2) Two pain points to listen for, 3) A one-sentence positioning statement for how our solution addresses long sales cycles. Format as bullet points."

Why it works: Provides context about the prospect, specifies what information to include, and requests a specific output format.

Example 3: Engineering

Weak prompt: "Explain this code"

Strong prompt: "Review this Python function and explain: 1) What it does in one sentence, 2) Any potential bugs or edge cases, 3) How it could be refactored for better readability. Assume I'm a mid-level engineer familiar with Python but new to this codebase. Keep explanations clear and suggest specific code improvements.

[paste code here]"

Why it works: Breaks down the review into specific questions, defines the audience's skill level, and asks for actionable improvements.

Frequently asked questions (FAQs)

What's the difference between a prompt and a query?

A query matches keywords to find existing information (like a Google search). A prompt instructs a generative AI to create new content based on patterns it learned during training. Prompts need more context and structure to produce useful results.

Can I reuse prompts across different AI models?

Usually, yes. But results may vary. Different models (GPT-5, Claude, Gemini) have different strengths and behaviors. A prompt optimized for one might need tweaks for another. Start with the same prompt and adjust based on the output.

How long should a prompt be?

As long as it needs to be. Simple tasks might need one sentence. Complex, recurring workflows benefit from detailed instructions with examples and constraints. Start short and add detail only when the output doesn't meet your needs.

What is prompt engineering?

Prompt engineering is the practice of designing, testing, and refining prompts to consistently get high-quality outputs from AI models. It combines clear communication, domain knowledge, and iterative testing to bridge the gap between what you want and what the AI produces.

How is writing prompts in Dust different from using LLMs?

In ChatGPT or Claude, you write one-off prompts for single conversations. In Dust, you build reusable agents with standing instructions that run consistently across your team. Dust agents also connect to your company's actual data sources (Google Drive, Notion, Slack) and can search, retrieve, and cite information from your knowledge base—not just generate answers from training data.

Do I need technical skills to build agents in Dust?

No. Dust is designed so non-technical teams can build agents using plain language instructions and the same prompting principles covered in this guide. The platform provides frameworks, suggestions, and debugging tools that make agent building accessible without coding expertise. That said, technical teams can also build more advanced agents using custom integrations and API connections when needed.