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Validating GenAI Features: Our Approach to Ensuring Accuracy

Validating GenAI Features: Our Approach to Ensuring Accuracy

As Voicebrook continues to push the boundaries of Generative AI (GenAI) in pathology reporting, ensuring the accuracy and reliability of our AI features is paramount. Our validation process leverages powerful tools and datasets to test, refine, and perfect the AI models we develop. Here’s an inside look at how Voicebrook approaches this critical step.

The Role of Datasets in Validation

One of the key elements of our validation process is the use of comprehensive datasets. These datasets allow us to simulate a wide range of real-world scenarios, providing large amounts of test data to ensure our models can handle various inputs and edge cases. This approach enables Voicebrook to test different prompts quickly and efficiently, offering valuable insights into how the AI will perform in practical use.

Each test is scored based on its accuracy against a ground truth, giving us an objective measure of how well the AI model is performing. By comparing the AI's output to what we know to be correct, we can identify areas for improvement and make necessary adjustments before releasing the feature to users.

Rapid Testing with Different Prompts

In addition to validating datasets, Voicebrook runs various prompt tests to ensure the Generative AI responds correctly to different inputs. This flexibility allows us to refine the AI's responses, ensuring they meet the specific needs of our users. The ability to rapidly test and adjust prompts is one of the key ways we ensure that our GenAI features provide the highest possible accuracy and reliability.

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Experiments for Continuous Improvement

While we’ve focused heavily on datasets and prompt testing, experiments are another vital tool in our validation process. By running controlled experiments, we can systematically test new ideas, configurations, and features to see how they impact the performance of our AI models. This iterative process of experimentation helps us continuously improve our GenAI solutions, ensuring they evolve and remain at the forefront of pathology reporting technology.

Refining Our First GenAI Feature

Voicebrook received very positive initial feedback on DraftDiagnosis, our first Generative AI feature, but some users felt that the output could be further refined. To address this, we are now running the feature through our enhanced validation process, using datasets and experiments to refine the output and improve its accuracy. This ensures that even our most established features continue to evolve based on user feedback and the latest technology.

Accuracy and Innovation Go Hand in Hand

“What I love about these tools is we aren’t in the dark when it comes to how GenAI will behave,” says Melanie Shedd, Voicebrook's VP of Product. “Since we started using these tools, we’re able to move faster to validate that GenAI can solve the problem, giving us the confidence to deliver solutions more quickly.”

By rigorously validating our Generative AI features through datasets, prompt testing, and experiments, we can confidently deliver solutions that meet the high standards of accuracy required in pathology. As Voicebrook continues to innovate, these validation methods will ensure our AI models remain reliable and effective for all users.

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Want to learn more about Voicebrook's Generative AI features for pathology?

Check out these recent blog posts on DraftDiagnosis, our upcoming GenAI features in development, and the way we ensure data security in our AI.

 

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