What is Prompting?

Prompting is the process of sending an advice or question to the AI and to get an answer back:

In the context of AI language models, prompting refers to the way in which you phrase or structure a question, statement, or request to guide the AI in generating a relevant and accurate answer. The prompt is the input provided to the model that defines the task or the type of information you're looking for.

It is very important to write a good prompt in order to get a useful answer back. The process of finding the best prompt for your use case in order to get the required answer is called Prompt Engineering.

PIPEFORCE provides many tools to write, engineer and execute prompts, chain them in pipelines and supervise them. In this chapter you will learn about the basic prompt principles and how to executed them.

Simple prompt

One of most generic and simplest use cases is to send an advice to the AI and use the response data in your pipeline. For this you can use the ai.agent.call command with a simple prompt question.

Here is an example to return some data from the AI using a pipeline:

pipeline:
  - ai.agent.call:        
      prompt: "Return the names of the 10 biggest cities in the world as JSON array."

Or as an RESTlike API call:

POST https://host/api/v3/command:ai.agent.call
Return the names of the 10 biggest cities in the world as JSON array.

This will result in an entry like this in the body:

[
    "Tokyo",
    "Delhi",
    "Shanghai",
    "Sao Paulo",
    "Mumbai",
    "Beijing",
    "Mexico City",
    "Osaka",
    "Cairo",
    "Dhaka"
]

As you can see, you can send a prompt to the AI using the command ai.agent.call. This command can be called with ad-hoc data or by refering to a reusable agent template. For more information about the full power of AI Agents in PIPEFORCE, see AI Agents.

Prompt with context

You can also apply the prompt with context data. This context data can be set as input to the command:

pipeline:
  - ai.agent.call:
      input: |
        [
          "Tokyo",
          "Delhi",
          "Shanghai",
          "Sao Paulo",
          "Mumbai",
          "Beijing",
          "Mexico City",
          "Osaka",
          "Cairo",
          "Dhaka"
        ]
      prompt: |
        Order the given list alphabetically.

The result of this example in the body is then:

[
    "Beijing",
    "Cairo",
    "Delhi",
    "Dhaka",
    "Mexico City",
    "Mumbai",
    "Osaka",
    "Sao Paulo",
    "Shanghai",
    "Tokyo"
]

The input of the command will become the context data. It can be plain text, a file or an URI. In case it is a file (for example a PDF or Word document) or any other supported format, PIPEFORCE will automatically convert it into an AI compatible format.

Here is an example which uses a PDF file as file context, stored in PIPEFORCE’s Drive cloud storage:

pipeline:
  - ai.agent.call:
      input: $uri:drive:invoice-3662.pdf
      prompt: |
        Check the invoice to ensure it is correct both in terms 
        of content and calculations. If everything is fine, return "OK". 
        If not, provide the reason for the error in one sentence.

Convert data with a prompt

You can also convert from one data structure into another using a prompt.

See this example which converts a given XML input to JSON:

pipeline:
  - ai.agent.call:
      input: |
        <person>
          <firstName>Max</firstName>
          <lastName>Smith</lastName>
          <age>36</age>
        </person>
      prompt: "Convert to JSON"

And the result from the AI in the body will be this:

{
    "person": {
        "firstName": "Max",
        "lastName": "Smith",
        "age": 36
    }
}

Filter data using a prompt

You can also use a prompt as a data filter.

Here is an example which uses a data privacy filter:

pipeline:
  - ai.agent.call:
      input: |
        {
          "person": {
            "firstName": "Max",
            "lastName": "Smith",
            "age": 36
          }
        }
      prompt: |
          Remove all personal data because of privacy and 
          replace by randomized names and add prefix p_

As a result, a changed JSON comes back:

{
    "person": {
        "firstName": "p_Alex",
        "lastName": "p_Johnson",
        "age": 48
    }
}

Prompt variables

You can make prompts more dynamically by using prompt variables.

Inside a prompt you can specify a {{variable}}. This variable will be replaced by its value before it gets sent to the AI. Here is an example:

pipeline:
  - ai.agent.call:
      prompt: "Translate this text to {{language}}: {{text}}"
      variables:
        language: "German"
        text: "Hello world!"

Prompt variables and Pipeline Expressions (PEL)

Do not mix-up prompt variables like {{myvar}}with Pipeline Expressions (PEL) like ${vars.anotherVar}. By default, the pipeline expressions will be executed first, when the pipeline gets loaded. The prompt variables will be interpolated when the agent is executed, so after the pipeline expressions have been executed.

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Prompting