Test and refine prompts for a Large Language Model

Large Language Models (LLMs) are a category of machine learning models that generate human-like text. They are trained on extensive amounts of data and are capable of understanding and generating responses based on given inputs as well as powering conversational experiences

We started AirOps to make it easier to build powerful solutions with these models as they play a growing role in business operations.

How to Configure an LLM Step

When setting up an LLM Step, there are multiple parameters to configure to best fit your use-case. We'll provide a brief overview of each parameter here.

Select an AI Model

Selecting a model depends on context window, task complexity, cost, and speed. Generally, more capable models offer higher quality output at a higher cost and slower speed.

For guidance on which model to select, check out our doc page on Choosing a Model.

You can also choose to bring your own fine-tuned model by connecting it via our API Providers page.


Temperature determines the amount of variability in the AI's response:

  • For greater variability and more "creative" responses, choose a higher temperature.

  • For less variability and more deterministic responses, choose a lower temperature.

Max Length (optional)

Limits the maximum # of output characters.


Streaming allows you to see output generated token-by-token, similar to the ChatGPT experience. The text will stream in the Test panel of the App Studio or when you run your app in AirOps.

Reasons for Streaming:

  • Rapidly test prompts: If you want to test prompts quickly, you can watch your output generate in real-time and validate your prompt faster instead of waiting for the entire response to finish.

  • Deliver an API with streaming: If streaming text in real-time is a better user-experience for your end-user, you should consider streaming as an option. On the other hand, if you're generating large amounts of text, perhaps streaming is not the ideal option for your end-user.

  • AirOps Frontend SDK: Our SDK makes it possible for you to stream Workflow outputs directly into your user experience

Consistent Results

Enabling the "Request Consistent Results" will request that the model make a best effort attempt to achieve consistent output results given the same input multiple times.

This can be useful for testing purposes, as maintaining consistency in results is often a requirement as you test the broader workflow.

Prompt Your Model

Once you've completed selecting your model and choosing the desired parameters, the next big step is to provide it with a prompt. Prompts should be as thorough and descriptive as possible, and we recommend referencing our doc pages on how best to "Prompt with GPT" and "Prompt with Claude."

We also encourage you to look through our suite of existing Templates. Nearly all of them include an LLM Step, and can help give some inspiration and guidelines for what your prompt may look like, e.g.

User Prompt

The "User Prompt" field provides the model with sample inputs or conversation to get the desired output, e.g.

Please write an exciting poem targeted toward kindergarten students with the following:
Poem Title: The First Day of School
Topics: Recess, Art Class, Snack Time

Assistant Prompt

The assistant prompt helps the model "learn" the desired output format. This is especially helpful to generate arrays, JSON, or HTML without extra "chat" text ("Sure! This is an array..."), e.g.

System: You output an array of 5 strings with nouns that are kitchen objects.

User: Give me an output of 5 utensils

Assistant: ["spoon", "fork", "knife", "spatula", "chopsticks"]

User: Now, give me an array of 5 {{my_input}}

In AirOps, you can include multiple User / Assistant pairs in order to give the LLM examples of how to respond.

Generate with AI

When configuring your LLM Step, you can use our "Generate with AI" tool to assist you in filling in the prompt:

We'll provide a quick breakdown of each of the customizable options that come with this feature, and share a walkthrough example below.

Note: "Generate with AI" is currently only compatible with OpenAI

Task Type

You first have to define your Task Type. The list of currently supported types includes:

  1. Content Creation -- a good choice if you are looking to generate content, e.g. a blog post, an image, a marketing email, etc.

  2. Question + Answer -- choose this option if you plan for your input to be a question and your output to be the answer

  3. Entity Extraction -- choose this option if you're looking to extract specific values from a text or image

  4. Text Extraction -- similar to Entity Extraction, this can be useful for parsing out specific text values

  5. Classification -- choose this option if you would like for your output to group different values of your input into similar categories

Output Type

Once you've defined your Task Type, the next decision to make is how you would like the LLM Step to output the results. The list of currently supported Output Types includes:

  1. Plain Text

  2. Markdown

  3. HTML

  4. JSON

  5. YAML

By selecting a value here, you can save yourself the additional effort of specifying the output type in your prompt (or requiring a subsequent Text Step to reformat the output)


The Prompt section is similar to what we covered earlier in "Prompt Your Model." However, the key difference here is that we can rely on our AI to flesh out the description further. So, if you were unsure of exactly how to phrase your prompt, OpenAI will attempt to expand upon it.

Advanced Settings

  • Output Example (optional) -- if the AI is struggling to output the results in the specified format, you can manually enter an example here for it to work from. This is similar to the User/Assistant Prompts above.

  • Selected Variables -- populates with any input variables you have leading into the step


As an example, let's walk through using the "Generate with AI" feature to help create a basic application. We'll imitate the Restaurant Review we created in our Workflow Quick Start.

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