Generative Artificial Intelligence (AI) is not built to deliver accurate, context-specific information for a particular task. As a result, it often falls short when it comes to B2B requirements, where false information masquerading as truth can cause severe damage to business enterprises. The key to enterprise-ready generative AI is to structure data rigorously to provide contextual relevance, which can be leveraged to train highly-refined large language models (LLMs) for generative AI to provide B2B enterprise value.
Three vital frameworks need to be incorporated into a company’s technology stack before taking advantage of generative AI’s unlimited potential. Firstly, a system must be trained on high-quality business-specific data, regularly monitored by humans, and updated over time to correct errors and improve accuracy. Secondly, an AI’s beautiful writing must be plugged into a context-oriented, outcome-driven system, including rigorous LLM fine-tuning. Companies then have a choice between a mix of hard-coded automation and carefully fine-tuned LLMs. Finally, companies must automate cumbersome manual tasks that are better suited for AI than people.
LLMs are black boxes that are still somewhat of a mystery, although some companies are bringing clarity to model standardization and efficacy evaluations. For example, Gentrace links data back to customer feedback, while Paperplane.ai captures generative data and links it with user feedback, allowing leaders to evaluate deployment quality, speed, and cost over time. By carefully structuring data, companies can build a strong anti-hallucination framework that allows generative AI to deliver their intended results with confidence in B2B scenarios.
