An hallucination occurs when a generative AI model produces incorrect, misleading, or illogical information. These mistakes can manifest in various ways, such as factual inaccuracies or outputs that appear plausible but have no real foundation. This article explains how Ask AI avoids the errors that users may commonly encounter when using other generative AI tools directly.
Leveraging Generative AI in Specific Ways
How is Ask Quid different from other generative AI tools? Ask Quid incorporates a conversational AI system in specific parts of its process but does not depend on this system for generating answers. Quid adopted this deliberate strategy in designing and training Ask Quid to minimize AI hallucination errors.
Ask Quid features three separate components working together to achieve this:
The user asks questions and receives answers in natural language within a chat interface.
Ask Quid uses a conversational AI model to transform a user’s question into one or more Quid queries.
Ask Quid then runs these queries against the Quid repository of clean, real-time consumer and market intelligence data.
Separate Data and a Transparent Approach
Other generative AI tools pull their data from the same datasets they were trained on, making them susceptible to outdated or even fabricated information. Ask Quid, however, derives its insights and answers from Quid’s extensive repository of social data, premium news, earnings calls, and other datasets, all of which contain data that is recent, comprehensive, and relevant to the task. Quid’s datasets are separate from those used to train the generative AI model, which greatly reduces the risk of generating incorrect or fabricated responses.
Additionally, many generative AI tools function as black boxes, where users cannot see the queries or the data being used to generate answers. Ask Quid, on the other hand, offers a more transparent approach, allowing users to examine the queries that poll each dataset. It then employs generative AI to translate that data into natural language, making insights more accessible to the user.
The Ask Quid Cycle
Let’s take a closer look at the entire process:
1. The user poses a question in Ask Quid.
2. Generative AI converts the user’s question into one or more Quid queries and uses these queries to access Quid’s datasets.
3. Ask Quid retrieves the necessary metrics and relevant data required to answer the question from clean datasets.
4. Generative AI synthesizes this data into insights to provide an answer to the user’s question.
The result is a natural language response to the user’s question, with full visibility into the queries and the underlying data. You can be confident that this data is hallucination free because:
Quid data is comprehensive, recent, and relevant
Quid combines a thoughtful and deliberate approach to using gen AI with other engineering solutions
Quid provides transparency, allowing users to see the actual queries used to poll each dataset
What’s Next
Interested in learning tips and tricks for crafting effective Ask Quid prompts? See Ask Quid Best Practices.