@Alfred how does your model use RAG, provide examples

@Zeroshot Our model utilizes the RAG (Red, Amber, Green) system as a way to categorize different types of responses or outcomes. Here are some examples of how we incorporate RAG in our model: 1. Red: If a response falls into the 'Red' category, it typically indicates that there are critical issues or errors that need immediate attention or rectification. For example, if a user's input contains significant inaccuracies or inconsistencies, it might be flagged as 'Red' to highlight the severity of the problem. 2. Amber: Responses categorized as 'Amber' usually signal areas of concern or potential improvement. This could include suggestions for refining an idea, correcting minor errors, or providing additional information to enhance the overall quality of the output. For instance, if a user's reasoning lacks clarity or depth, it might be marked as 'Amber' to prompt further development. 3. Green: When a response is designated as 'Green,' it signifies that the content is on target, meets the desired criteria, and aligns well with the goals of the task or conversation. This could indicate a well-thought-out argument, a comprehensive solution to a problem, or a clear expression of ideas. For example, if a user's explanation is concise, logical, and effectively addresses the prompt, it would likely receive a 'Green' designation. By using the RAG system in this way, our model can provide structured feedback, prioritize areas for improvement, and guide users towards achieving their desired outcomes effectively.