Choose this use-case, if you would like to develop a model that provides human-level responses that are specialized on your internal document corpus. This model would facilitate using a Large Language model to answer natural language question prompts to engage in conversational dialogue regarding institutional knowledge bases, such as training/onboarding resources; policies and procedures; product documentation; and intellectual property documentation.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a ChatLLM model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a ChatLLM model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Evaluating PredictionsThis section contains a quick model evaluation guide that helps you understand how well your model is performing.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.