Understanding the potential limitations of AI tools is important. Let's explore the main reasons why AI language models could generate inaccurate responses, as well as the measures Strategy AI has implemented to reduce such inaccuracies.
- Sensitivity to Input: The phrasing and structure of the input prompt can greatly influence the output. Slight changes in how a question is asked can lead to different responses.
Strategy Chat provides enhanced functionalities, including Chat Settings, which furnish users with supplementary guidance and structure for their prompts. Additionally, Strategy AI provides sessions for prompt engineering coaching and is in the process of creating self-directed training modules, all designed to help users transition seamlessly from beginners to advanced practitioners. - Randomness in Outputs: To promote diversity in responses, these models introduce some level of randomness. While this can lead to more varied outputs, it can sometimes cause the model to provide inaccurate or unexpected responses.
At Strategy AI, we have coded Strategy Chat so that it reduces the randomness of responses. We have found a combination of prompt chaining, pre-processing, and post-processing can do a great job of mitigating the risks of hallucinations and improve the accuracy of Strategy Chat. More importantly, when using Strategy Chat, answers are sourced from internal files and documents. Hallucination is significantly reduced because of this fact. - Over-Optimization and Bias: The inherent bias in data is a combination of human bias and bias in data. And biased data therein affects the AI models that are trained on that data.
At Strategy AI, we guide users to curate appropriate data and ensure ethical practices are followed in collecting and cleansing the data. All users are custodians for preserving high-quality data. We believe data transparency is one of the best tools for identifying and correcting bias. Therefore, with each answer provided, we provide the excerpts and source documents that were used to develop the answer. By doing so, users can identify and report suspected cases of bias. Administrators of the data are notified and they are able to reevaluate the data. - Training Data Limitations: AI models learn from vast amounts of text data. If the data they're trained on contains inaccuracies, the model may replicate those errors. It's also limited to the quality and breadth of its training data. It cannot generate accurate responses about topics it wasn't adequately trained on.
At Strategy AI, we are constantly assessing AI models and implementing technological strategies to mitigate inaccuracies.
In Summary: AI Chats can be inaccurate due to factors like sensitivity to input phrasing, inherent randomness for diverse responses, biases in data, and limitations in training data. Strategy AI addresses these by enhancing chat functionality, reducing randomness, promoting data transparency, and updating AI models for accuracy.