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Your job is to generate the policy.md file for an automated agent for the {domain} domain. |
Specifically, you will be writing the policy for an automated chatbot agent that a customer to a business in the {domain} domain can interact with. |
Your task is as follows: |
1. Think about the {domain} domain and consider businesses that can span from that domain. |
2. Choose a specific business that can benefit from deploying a chatbot agent that the business' customers can interact with for business-related tasks. |
3. Deliberate about the tools that the chatbot agent can use to interact with an underlying system to resolve a customer's needs. Your tools can be in camel case or snake case. |
4. Create a policy.md file for the chatbot agent between <policy> </policy> tags. |
When writing the policy, consider: |
- If there is a need for the customer's identity to be verified, mention it in the policy. |
- Always include a way for the agent to escalate the user's concerns to a real human or transfer to a human agent. |
- Including the current time in the policy the model can refer to, which is {timestamp}. |
- Adding a statement to avoid answering questions that the chatbot isn’t qualified to handle—especially those that require advice not based on the policy. |
You must also include the following sections: |
1. A section for objects relevant to the policy: |
- For example, the idea of a user (e.g. with name, email, etc) and business-specific objects (e.g. reservation for retail/airline/etc, tickets for service, etc.) |
- This section does not have to be too detailed |
- Design the objects so that tool parameters can be primitives, not full objects. |
2. Sections for constraints and limitations relevant to interactions with such objects. |
- For example, if this is an airline policy, there must be constraints for reservation changes, seat changes, baggage policies, etc. |
- This is the most important part of the policy, and you must think hard and write a concise yet believable constraints. |
Do not include the following in the policy: |
- Any limitations to the agent regarding rate limits or performance. |
- Do not have the agent send emails or texts to the user, verification can be done by asking the user for their identifying information. |
- Tools that call other generative models such as pdfs / images / videos should not be added nor considered. |
- You do not need to tell the agent to be friendly or helpful, the agent will by default be firendly and helpful. |
- Information about how to log sensitive information does not need to be written as it is not handled by the autonomous agent. |
The policy.md is not a description of the business nor a helpful instruction to the agent. |
Instead, it is a strict description of *how* the model should act and *what* constraints there are to its capabilities. |
Make sure that the policy has an emphasis on customer service related to the business in the domain {domain}. |
Do not make the generated policy sound like an example template. Make it highly specialized for the specific chatbot and business as if this were a real policy that could be used immediately. |
NeMo Gym Conversational Tool-Use Assets
This dataset repository stores prompt and reference assets for NeMo Gym's conversational tool-use generation pipeline. It is an asset bundle for Gym components, not a training or evaluation dataset.
Contents
conversational_tool_use_domain_generation/prompts: the domain-generation prompt.conversational_tool_use_domain_generation/prompt_history: historical domain-generation prompt revisions.conversational_tool_use_policy_tool_generation/prompts: policy, tool, refinement, and judge prompts.conversational_tool_use_policy_tool_generation/prompt_history: historical policy/tool-generation prompt revisions.conversational_tool_use_policy_tool_generation/references/golden_policies: golden policy Markdown and paired tool JSONL references.conversational_tool_use_scenario_generation/prompts: scenario-generation system and user prompts.
The rollout simulation server and policy agent keep their prompt templates in their Python implementations. Those templates are not duplicated in this dataset. JSON schemas, runnable examples, package documentation, and Python requirements remain in Git.
Run the conversational tool-use simulation resource server's preparation command once per checkout to download and checksum-validate the runtime assets:
python -m resources_servers.conversational_tool_use_simulation.prepare
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