Best Practices for rtrvr.ai
This page outlines best practices for using rtrvr.ai effectively. By following these guidelines, you'll be able to optimize your workflows, ensure accurate results, and make the most of the AI agent's capabilities.
General Tips
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Seperate Chats: Create new chats for new tasks so that the agent's context isn't polluted from prior tasks.
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Cancel Ongoing Requests/Workflows: Click "Stop" or close the Extension to cancel or stop any ongoing workflows or requests.
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Be Specific: The more specific you are with your commands, the better rtrvr.ai will understand your intent. Use precise language when stating the task you want to perform or data you wish to extract.
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Use Natural Language: Don't hesitate to use everyday language in your prompts. rtrvr.ai is designed to interpret natural phrasing effectively.
Usage Examples
Simple Actions
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Example: On linkedin.com, you can use the command:
"Find rtrvr.ai's LinkedIn page and follow the company."
This will find the company page and follow the page. - →
Chat with Websites/Documents: rtrvr.ai can interact with local PDF files, remote PDF files, and regular websites. You can ask questions and perform actions on these documents/websites.
Crawl Listings
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Amazon Products: If you are on an Amazon product listing page you can command the agent to extract the name, price, sale discount for each product. rtrvr.ai will opens each product page and extracts data.
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Local Directory Usage: To use the local directory feature you can use a file path as a URL, for example
"file:///Users/<user>/Documents/test_pdf/"
. You can extract information from files using a command such as:"For every PDF in the directory, extract paper authors, a summary of the article, model architecture, model size, and overall accuracy."
Troubleshooting
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If rtrvr.ai is not responding as expected: Ensure that the extension has been enabled and re-try the command, or try rephrasing your prompt for better understanding.
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For complex tasks: Break down complex tasks into simpler steps, or try using recordings to train the model with a practical example.