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tanya rai
tanya rai

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Reduce Hallucinations 💤

Hallucinations are a big problem when working with LLMs.

We made a simple Colab template to help you verify your responses from LLMs.

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The template uses Chain-of-Verification (CoVe), a prompt engineering technique developed from research at Meta AI. CoVe enhances the accuracy of LLMs by generating a baseline response to a user query and then validating the information through a series of verification questions. This iterative process helps to correct any errors in the initial response, leading to more accurate and trustworthy outputs.

The template is backed by AIConfig - a JSON serializable format for models, prompts, and model parameters. You can easily switch out the models used for this template and use the AIConfig directly in your application code.

Try it here: colab notebook

Please star our repo for AIConfig! It's our first open-source project and we're thrilled to share it with you. ⭐️

API Trace View

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Struggling with slow API calls? Dan Mindru walks through how he used Sentry's new Trace View feature to shave off 22.3 seconds from an API call.

Get a practical walkthrough of how to identify bottlenecks, split tasks into multiple parallel tasks, identify slow AI model calls, and more.

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Top comments (3)

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Andrew Jensen

interesting topic. I want to know more!
Where can I read up about this?

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XXXX

Thank for sharing!

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