Compile weekly legal questions from a Slack channel
An in-house counsel is building a ChatGPT agent that scans a Slack channel weekly and lists legal questions the team has about the Terms of Use. Task: Suggested implementation: An in-house counsel proposes using a ChatGPT agent to weekly scan the #legal Slack channel for Terms of Use questions. The agent compiles these into a section-by-section list for human review and potential policy updates. Human involvement is critical for verifying classification accuracy and finalizing document changes.; tools: ChatGPT. Human role: The original post does not say how a person stayed involved.; limitations: Accuracy is the primary constraint, requiring significant prompt engineering or fine-tuning to filter unrelated queries. The source is a secondary Reddit post with no verifiable execution data. Human approval is mandatory before any policy updates, as the AI cannot autonomously determine legal relevance..
Tools
Published on Agent Work Atlas:
Overview
An in-house counsel is building a ChatGPT agent that scans a Slack channel weekly and lists legal questions the team has about the Terms of Use.
Workflow
Suggested implementation: An in-house counsel proposes using a ChatGPT agent to weekly scan the #legal Slack channel for Terms of Use questions. The agent compiles these into a section-by-section list for human review and potential policy updates. Human involvement is critical for verifying classification accuracy and finalizing document changes.
Reported result
The workflow is currently unreliable and unproven. The agent frequently misclassifies non-Terms of Use legal issues, and the author remains uncertain about the long-term viability of this approach. No successful outcomes or efficiency metrics have been reported.
Human role
The original post does not say how a person stayed involved.
Limitations
Accuracy is the primary constraint, requiring significant prompt engineering or fine-tuning to filter unrelated queries. The source is a secondary Reddit post with no verifiable execution data. Human approval is mandatory before any policy updates, as the AI cannot autonomously determine legal relevance.
Try it
Act as a legal operations assistant. I will provide a weekly export of our #legal Slack channel in [replaceable: JSON/CSV format]. Your task is to identify questions specifically related to our Terms of Use. 1. Parse the messages and extract only ToU-related inquiries. 2. Group them by relevant ToU sections. 3. Output a Markdown table with columns: 'Original Message', 'Identified ToU Section', 'Confidence Score (1-5)'. 4. Flag any ambiguous items for human review. Do not generate policy updates. First, process a sample of 10 messages I will provide. Wait for my confirmation that the classification logic is correct before processing the full export. If the sample accuracy is below 80%, stop and ask for clarification on ToU scope.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.