In today's technology-driven world, AI model training and management are indispensable for enhancing the performance of search features like Alli, and by incorporating training data, user feedback, and dictionary items, we can take the accuracy and relevance of our AI models to the next level.
AI model training and management is crucial for optimizing the performance of our document search feature of Alli. We provide AI models, like Alli, that come pre-trained with domain-specific publicly available documents, however, further training is always a good idea to improve accuracy and relevance. In this blog post, we will discuss the importance of adding training data, user feedback, and dictionary items in AI model training and management.
Adding Training Data
Adding new training data is an effective way to fine-tune AI models. The more diverse and relevant the training data, the better the model's performance. Retraining the model with additional data can improve its accuracy, reduce bias, and help the model generalize better. Our AI models can be easily retrained using custom data from your documents to suit your specific needs.
Adding Dictionary Items
Adding dictionary items can help the AI model to identify acronyms, industry-specific terms, and technical jargon. This is particularly important for specialized industries where the language can be very technical and domain-specific. We recommend our clients to provide a list of industry-specific terms and acronyms to add to the model's dictionary, allowing it to better understand the context in which these terms are used.
For more information on how to properly manage these items within the dashboard, please refer to our user guide documentation found here.
User feedback is another valuable source of information for AI model training. Feedback from end-users can help identify areas where the model is performing poorly and provide insights into user needs. We encourage our clients to collect user feedback by utilizing the thumbs up and down function to further fine-tune their AI models. User feedback is almost as good as adding new training data since it also helps to improve the model's accuracy and relevance.
Adding training data, user feedback, and dictionary items are essential for AI model training and management. These approaches can help fine-tune AI models, improve accuracy and relevance, and reduce bias. By prioritizing AI model training and management, our clients can achieve more accurate and relevant results, beyond the high baseline accuracy we provide out of the box.
If you wan to learn more about Allganize's solutions, please contact us.