Frequently asked questions

GenAI Activities use Integration Service to allow you direct access to UiPath-managed models via the UiPath AI Trust Layer. Integration Service requires a connection to execute authenticated API calls and to provide governance around which connections the robot uses at run time. GenAI Activities are based on Integration Service, thus require a connection. You are asked to create this connection only once.

GenAI Activities give you access to managed subscriptions for popular foundation models from a variety of vendors. This means that you don’t need to commit to cloud service providers or manage infrastructure. Instead, you can focus on reaping the benefits of GenAI decisioning directly into your workflows and optimizing for the highest quality results.

GenAI Activities offer quality-tested, prompt-based activities with simple inputs and outputs to accomplish common RPA tasks like categorization, summarization, image comparison, etc.

All calls are routed through the UiPath AI Trust Layer, so you benefit from governance controls and audit capabilities. In the AI Trust Layer Automation Ops policy, you can choose to disable the GenAI Activities for a particular tenant. This ensures no user or robot is able to execute requests. You can also set up an Integration Service Automation Ops policy to hide the entire UiPath GenAI Activities for tenants, groups, or users in your organization.

No. You can use on-premises robots that have access to Orchestrator in the Cloud, but GenAI Activities, AI Trust Layer, and Context grounding are currently available only in Automation Cloud.

If you don't see the GenAI Activities in Studio Desktop or Studio Web, make sure you:

Errors may occur for the following reasons:

Rate limiting: UiPath has instituted safeguards to ensure that GenAI Activities are not abused or misused. You may encounter a rate limit error, calculated based on the total tokens (roughly corresponding to word count) submitted within a certain time interval. If you encounter this error, reconfigure your workflow to include multiple GenAI calls with smaller context windows, separate GenAI Activity executions across multiple workflows, or add delays in the workflows. If you consider this error has occurred without reason or is blocking your adoption of GenAI Activities, contact your account management team.

Automation Ops policy: An AI Trust Layer Automation Ops policy can block your tenant from executing activities. Contact your administrator and make sure the policy is disabled in order to use the activities.

Context window exceeded: All models supported in the GenAI Activities have very explicit context windows, which means that only a text string of a certain length is supported. Check the LLM vendor documentation to see what the individual model limits are (expressed in tokens, but can easily be converted to word count). Consider the length of your inputs expected at run time. Another option is to use Context grounding to analyze content that may exceed the context window for a single request.

Context grounding errors: See the Context grounding Frequently asked questions and ensure that you are following best practices, particularly regarding the supported file types.

UiPath gives you access to quality-proven, enterprise-ready models in a provider-agnostic manner. The Content Generation and Image Analysis activities allow you to select models that UiPath has licensed and validated for GenAI Activity use. If there is a model that you and your organization would like supported, reach out to your account team or contact us via the UiPath Community forum.

For the Content Generation or Image Analysis activities, where you control the prompt directly, the most likely cause for a suboptimal output is that the prompt is not worded appropriately or critical variables/arguments are not correctly populated. Use a model to help you better word your prompt by explaining your use case, your expected output, and the prompt you’re using. Poorly worded or unclear prompts can result in unexpected results.

Ensure you test your workflows extensively, particularly so with GenAI Activities. Make sure to log messages and that variables and arguments are correctly displayed.

If you're using Context grounding, make sure the index that you are referencing has been properly ingested. If you're using a file, make sure the file is in a supported format.

Your prompt may be cut off due to hitting a context window limit.

Activities such as Categorize, Named Entity Recognition, or Object Detection are designed to deliver the best output for a given task using a pre-tested prompt and model combination. All of these tasks can be accomplished using a custom prompt as well, if you require more control over model configuration.

GenAI Activities consume AI Units on a per-execution basis. Each GenAI Activity execution, regardless of token-size, is charged 1 AI Unit against the available allotment.

If you're using Context grounding, irrespective of whether an existing index or file, the execution costs 2 AI Units. So each GenAI Activity execution (1 AI Unit) + Context grounding (1 AI Unit) = 2 AI Units per execution where Context grounding is in use.

The AI units consumption associated to the use of GenAI Activities is displayed in Automation Cloud Admin, on the Consumables tab of the organization- and tenant-level Licensing page.

Reach out to your account management team to acquire AI Units.

Context Grounding is currently available in the Content Generation activity. You can use an index (a repository of documents upon which the prompt is grounded) or upload a file for a just-in-time Context Grounding. It works by taking all of the data submitted in the prompt and searching through the index or file submitted for contextually relevant data to ground the prompt in.

With GenAI Activities you can save on development time and build what would otherwise be very complex, deterministic workflows. There is a trade-off, however, in that all GenAI outputs are inferences. You can optimize inferences with better instructions (prompting) provided to the model, higher quality inputs/variables, and grounding your queries and tasks in an appropriate context.

However, we recommend you institute validation steps whereby a robot loops in a human to ensure that the GenAI output was accurate. This doesn't have to happen for all workflows. In many cases you can incorporate a score or evaluation threshold that would warrant a human-in-the-loop (perhaps by using a GenAI activity for evaluation). For human validation, UiPath has a variety of options available:

You can use the Integration Service third-party connectors to connect to popular cloud Services like Azure OpenAI, OpenAI, etc.

Generative AI can make very complex tasks relatively simple for RPA developers to accomplish. With GenAI Activities, working with LLMs in the context of Studio, Studio Web, Apps, etc., becomes simple. Check out the following list to see what you can do with GenAI activities: