---
title: Prompts overview and use cases
short: Overview and use cases
tier: enterprise
type: guide
order: 0
order_enterprise: 225
meta_title: Prompts overview
meta_description: An overview of the Prompts feature on the HumanSignal platform
section: Prompts
Use Prompts to evaluate and refine your LLM prompts and then generate predictions to automate your labeling process.
All you need to get started is an LLM deployment API key and a project.
With Prompts, you can:
!!! note
For information on installing Prompts for on-prem environments, see Install Prompts.
Prompts allows you to leverage LLMs to swiftly generate accurate predictions, enabling instant labeling of thousands of tasks.
By utilizing AI to handle the bulk of the annotation work, you can significantly enhance the efficiency and speed of your data labeling workflows. This is particularly valuable when dealing with large datasets that require consistent and accurate labeling. Automating this process reduces the reliance on manual annotators, which not only cuts down on labor costs but also minimizes human errors and biases. With AI's ability to learn from the provided ground truth annotations, you can maintain a high level of accuracy and consistency across the dataset, ensuring high-quality labeled data for training machine learning models.
If you don't already have one, create a project and import a text-based dataset.
Annotate a subset of tasks, marking as many as possible as ground truth annotations. The more data you have for the prompt evaluation, the more confident you can be with the results.
If you want to skip this step, see the bootstrapping use case outlined below.
Go to the Prompts page and create a new Prompt. If you haven't already, you will also need to add an API key to connect to your model.
Write a prompt and evaluate it against your ground truth dataset.
When your prompt is returning an overall accuracy that is acceptable, you can choose to apply it to the rest of the tasks in your project.
In this use case, you do not need a ground truth annotation set. You can use Prompts to generate predictions for tasks without returning accuracy scores for the predictions it generates.
This use case is ideal for organizations looking to kickstart new initiatives without the initial burden of creating extensive ground truth annotations, allowing you to start analyzing and utilizing your data immediately. This is particularly beneficial for projects with tight timelines or limited resources.
By generating predictions and converting them into annotations, you can also quickly build a labeled dataset, which can then be refined and improved over time with the help of subject matter experts. This approach accelerates the project initiation phase, enabling faster experimentation and iteration.
Additionally, this workflow provides a scalable solution for continuously expanding datasets, ensuring that new data can be integrated and labeled efficiently as the project evolves.
!!! note
You can still follow this use case even if you already have ground truth annotations. You will have the option to select a task sample set without taking ground truth data into consideration.
If you don't already have one, create a project and import a text-based dataset.
Go to the Prompts page and create a new Prompt. If you haven't already, you will also need to add an API key to connect to your model.
When you run your prompt, you create predictions for the selected sample (this can be a portion of the project tasks or all tasks). From here you have several options:
As you evaluate your prompt against the ground truth annotations, you will be given an accuracy score for each version of your prompt. You can use this to iterate your prompt versions for clarity, specificity, and context.
This accuracy score provides a measurable way to evaluate and refine the performance of your prompt. By tracking accuracy, you can ensure that the automated labels generated by the LLM are consistent with ground truth data.
This feedback loop allows you to iteratively fine-tune your prompts, optimizing the accuracy of predictions and enhancing the overall reliability of your data annotation processes. In industries where data accuracy directly impacts decision-making and operational efficiency, this capability is invaluable.
If you don't already have one, create a project and import a text-based dataset.
Annotate a subset of tasks, marking as many as possible as ground truth annotations. The more data you have for the prompt evaluation, the more confident you can be with the results.
Go to the Prompts page and create a new Prompt. If you haven't already, you will also need to add an API key to connect to your model.
Write a prompt and evaluate it against your ground truth dataset.
