What is Better Than Chat GPT
The combination of GPT and Text Analytics! For all the success that GPT has accomplished in the public sphere, it has a number of limitations within the enterprise that text analytics can overcome – if done correctly.
The well-known issues with GPT include:
- Tendency to hallucinate, that is, make up false facts
- Trained on public information, but enterprise content and vocabularies are quite different
- Transparency – understand why it says what it does.
- Security Issues – sending or leaking sensitive information
- Amount of data needed to train
Best of Both Worlds
At the KAPS Group, we have developed a set of techniques that combine the best of both worlds. Our techniques employ a mutually enriching hybrid of text analytics and GPT that not only eliminates the most grievous drawbacks to GPT, but at a fraction of the cost of producing a custom enterprise LLM.
We then determine the best balance of the flexible generality of GPT with the depth and accuracy of TA for each application.
Our techniques begin with determining the best balance of the flexible generality of GPT with the depth and accuracy of text analytics for each application. We typically work from GPT general and then incorporate text analytics (auto-categorization, data extraction) at every development stage – producing much richer queries and processing the output while creating a set of rules that provide the often-needed transparency.
This results in a multi-dimensional platform that can be used to build multiple applications that reflect your world, not a generic and overly simplistic public world, fact-check hallucinations, provide human-understandable explanations, check security, and require much less data to train.
