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Building Prompts

How to create and configure prompts in Aisle.

Creating a Prompt​

Navigate to the Prompts page and click New Prompt.

You'll configure several key elements:

Name and Description​

Name - What your team will see when searching for this prompt. "Article Summarizer" is better than "Test_Prompt_3" because in three months when someone needs to summarize an article, they'll actually find this.

Description - Helps you remember what this does. More importantly, if you share this prompt in chat, your teammates see this description when deciding whether to use it. Write something useful: "Condenses long articles into two-sentence summaries" tells someone exactly what they're getting.

Icon - Purely visual, but when you've got twenty prompts in your library, icons help you scan faster.

Selecting Your Model​

Different AI models have different strengths:

  • Some prioritize speed for quick responses
  • Others excel at complex reasoning
  • Some are better for creative tasks
  • Others for structured data analysis

See the full list at app.aisle.sh/models.

Not sure which to use? Start with Claude 3.5 Sonnet or GPT-4—they're good all-rounders. You can always change this later (more on that in a moment).

Writing Instructions​

User Message - This is where the actual prompting happens. The instruction that runs every single time someone uses this prompt.

Start simple:

You are going to receive an article. I want you to summarize it to only two sentences.

This works, but it's minimal. In real use, you'd add more detail:

  • What should those two sentences focus on? Main argument? Key findings?
  • Should the tone be formal or casual?
  • Any specific format requirements?

The more specific you are about what you want, the more consistent your outputs will be.

System Message (optional) - Sets the overall behavior or role for the model. This is optional but useful for certain cases.

The system message is broader context ("you are a technical documentation expert" or "always respond in valid JSON format"), while the user message is the specific task ("summarize this article").

For a simple summarizer, you might not need this. But if you're building something like a customer support prompt where tone and format matter across many different questions, the system message is where you'd set that baseline.

Making Prompts Dynamic with Variables​

Variables make prompts reusable. Without variables, you'd need to edit the prompt every time you want to process different input.

Creating Variables​

Click "Add Variable" and rename it to something meaningful. For an article summarizer, article makes sense.

Reference it in your user message with double curly braces:

The article to process: {{article}}

Now when someone runs this prompt:

  • In chat - They see an input field labeled "article"
  • Via API - They pass {"article": "..."} in their request
  • In workflow - The variable connects to output from a previous node

The prompt stays consistent, the variable changes.

Multiple Variables​

You can have multiple variables:

Analyze this {{feedback_type}} from {{customer_segment}} focusing on {{focus_area}}:

{{feedback_content}}

Now your single prompt handles any type of feedback analysis. Sales team uses it for call summaries, support team for ticket analysis, product team for feature requests.

Model Configuration​

Model Settings​

Temperature - Controls randomness:

  • Lower (0.0-0.3): Consistent, predictable outputs. Good for data extraction, classification.
  • Higher (0.7-1.0): Creative, varied outputs. Good for brainstorming, writing.

Max Tokens - Limits response length. The model generates text token by token (roughly a token is a word or word fragment). Set this to control output length.

Enabling Tools​

Depending on the model:

Web Search - Lets the model pull current information from the internet. For an article summarizer, you don't need this because you're providing the article. But for a prompt that answers questions about recent events, you'd turn this on.

Allow File Uploads - Determines whether users can attach files when they run the prompt. Maybe your article summarizer should accept PDFs. Turn this on, and users can upload a PDF article instead of pasting text.

Citations - When enabled, the model cites which parts of your provided context it's pulling information from. Useful for research or legal use cases where you need to trace claims back to sources.

Attaching Files and Memories​

Attach Files - Give the model permanent context available every time the prompt runs.

Example: You're building a prompt that answers questions about your product. Attach your product documentation as a file here. Now every time someone asks a question, the model has access to that documentation without you needing to paste it into every single request.

Attach Memories - Load markdown files from your memories into any prompt. Think of memories as easy markdown storage for AI. You might have a memory containing your company's style guide, product specs, or terminology. Attach it here, and it's available every time the prompt runs.

The key difference from variables: files and memories attached here are permanent. They're part of the prompt itself. Variables are temporary—they change with each use.

Structured Output​

Some models support structured output, which forces the response into a specific JSON schema. This is incredibly useful when you're calling prompts via API and need to parse the response programmatically.

Instead of getting back natural language that you then have to parse, you get back a predictable JSON object every time.

Example schema:

{
  "summary": "string",
  "key_points": ["string"],
  "sentiment": "positive|negative|neutral"
}

Now every response fits that exact format. No parsing needed.

Display Message​

When someone runs your prompt in chat, they see the user message by default. If your user message is three paragraphs of detailed instructions, the chat gets cluttered fast.

The display message lets you show something cleaner:

Summarize this article

That's what appears in the chat. The model still receives your full detailed user message behind the scenes, but your chat stays readable.

Model Switching​

One of Aisle's most significant advantages: you can change a prompt's underlying AI model anytime without rebuilding anything else.

Simply select a different model from the dropdown. Your instructions, variables, sharing settings, and usage history all remain intact.

This means you can continuously upgrade your AI capabilities as new models become available, without the traditional overhead of migration projects.

Testing Your Prompt​

Before sharing with your team, test it:

  1. Fill in sample variable values
  2. Run the prompt
  3. Check the output
  4. Adjust instructions if needed
  5. Test again

Use realistic test data - Don't test with "test test test." Use actual examples of what the prompt will process in production.

Try edge cases - What happens with very short input? Very long input? Unusual formats?

Test different variables - If your prompt has multiple variables, try different combinations.

Versioning​

Every time you save changes to a prompt, Aisle creates a new version.

  • Changed the model? New version.
  • Tweaked the temperature? New version.
  • Rewrote the user message? New version.

This matters because prompts break. You'll make a change that seems smart, deploy it, and discover it's producing garbage outputs.

With versions, you can:

  • Roll back to yesterday's working version immediately
  • See exactly what changed between versions
  • Compare versions side-by-side in Playgrounds
  • Review who made what changes and when

You're not guessing what broke. You're looking at a diff.