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Flow

1

Start of with the prompt template. The prompt template should include:
  1. The signals you want to extract
  2. The data-context the model should use to extract data. (This will be done using functions)
2

After you have the prompt fill in signals.
  1. For each signal you need to fill in:
    1. Name - Will be the signal name
    2. Description - Explain the model exactly how to compute this step.
    3. Type - Boolean / String.
3

Run

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Functions

In order to add data context to the prompt we use functions. Functions are executed and added to the prompt prior to sending the prompt to the model.

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Supported functions

Parameters: query Description: Performs a Google search using the query string parameter and returns the results. Here is an example where we might wand to add context regarding company’s layoff: 
Here is some relevant context:
Company website:
{{Search(query=record['Webiste'] + ' layoffs')}}

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ReadUrlText

ReadUrlText(link): Retrieves and returns the text content from a webpage given its link.

## Example
Assuming dataset as a column named domain

Company search results:
{{ReadUrlText(link=record['domain'])}}

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LinkedinSearch

SearchLinkedin(query): Searches for specific entities (such as profiles or companies) on
LinkedIn based on the query.

Linkedin search results:
{{SearchLinkedin(query=record['domain'] + ' ceo')}}

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GetCompanyContacts

GetCompanyContacts(domain, job_titles, skills): Extracts and returns the contact information
of a company specified by domain, filtered by job_titles and skills.

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LinkQuestionAnswering

LinkQuestionAnswering(link, question): Analyzes a question, maps it to
a data model within the webpage at the provided url, and retrieves the answer.

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SearchForNaicsCodes

SearchForNaicsCodes(query, naics_code_level: Optional):
Searches for NAICS codes based on provided query (e.g., “cloud security”, “clothing ecommerce”).
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