Customizing Large Language Models in 2 steps via fine-tuning is a very efficient way to reduce data needs, as well as training and evaluation efforts, when building customized Conversational Assistants. Bitext provides these Pre-Built Datasets and Models in 20 verticals.

Discover the advantages of using symbolic approaches over traditional data generation techniques in GenAI. Learn how 100% reliable, bias-free, and PII-free data can be achieved through rule-based generation, ensuring semantic integrity and accuracy. Explore the unique benefits of this method for generating variations from seed sentences with predictable outcomes.

In the blog “General Purpose Models vs. Verticalized Enterprise GenAI,” the focus is on the advantages of verticalizing AI models for specific enterprise domains. Verticalized models can disambiguate context-specific terms and speak in industry-specific tones. There are two approaches: building models from scratch, which is costly, or fine-tuning general-purpose models with domain-specific data. Bitext proposes a faster two-step method: first, verticalize the model, then customize it with enterprise data. This approach saves time, resources, and avoids common AI issues like hallucinations and bias.

Bitext introduced the Copilot, a natural language interface that replaces static forms with a conversational, proactive, and highly personalized user experience. This change not only simplified the onboarding process but also made it more interactive and capable of resolving queries in real time, offering significant advantages over traditional methods.

Automating Online Sales with a New Breed of Copilots. The next generation of GenAI Copilots moves from passively answering customer questions to actively executing online sales. These new Copilots are proactive, they can start and drive an interaction with a potential customer; and context-aware, they know the different steps in the sales process, where they are in the process and how to move to the next step.

GPT and other generative models tend to provide disparate answers for the same question. Having control is called Fine-tuning.

ChatGPT has major flaws that prevent it from becoming a useful tool in industries like Customer Experience