In her 2023 TED Talk, computer scientist Yejin Choi stated that AI is both unbelievably intelligent and shockingly stupid. This apparent paradox arises because AI, including generative AI, is not designed to provide accurate, context-specific information for a specific task. Instead, generative AI models generate responses based on probable theories derived from their past experiences. Although generative AI can be creative, it often fails to meet B2B requirements, leading to the generation of false information that masquerades as the truth. The key to making generative AI enterprise-ready is to structure data rigorously to provide context for highly refined large language models (LLMs).
The Three Vital Frameworks for Incorporating Generative AI into Your Technology Stack
To make the most of generative AI’s vast potential, businesses must incorporate three vital frameworks into their technology stacks.
1. Train Generative AI on High-Quality Data
Generative AI should not work in a vacuum. Instead, it should be trained on high-quality data to produce accurate outputs. Feedback loops should be established to enable humans to monitor the system regularly and correct errors to improve model accuracy.
2. Use Hard-Coded Automation and Fine-Tuned LLMs
Generative AI’s writing must be plugged into an outcome-driven system that is context-oriented. Companies should use a mix of hard-coded automation and fine-tuned LLMs to create the most context-oriented outputs. Fine-tuning models involves training models on many case-specific examples to achieve better results. By leveraging each technology’s strength, companies can create structured facts and context that let LLMs do what they do best.
3. Keep Humans in the Loop
LLMs are black boxes, and the industry lacks standardized efficacy measurements. As a result, companies should keep humans in the loop to verify the accuracy of model outputs, provide feedback, and correct results if necessary.
Choosing the Right Tasks for Automation and Generative AI
Businesses should identify tasks that are computationally intensive for humans but easy for automation and vice versa. They should use automation to conduct grunt work, such as aggregating information and combing through company-specific documents, and hard-code definitive, black-and-white mandates such as return policies. Generative AI should only be deployed after setting up a strong foundation, as the inputs are highly curated before generative AI touches the information, and the systems are set up to tackle more complexity accurately.
Standardizing Efficacy Measurements
Companies such as Gentrace and Paperplane.ai are bringing clarity across generative AI models by standardizing efficacy measurements and linking data back to customer feedback. By capturing generative AI data and linking it with user feedback, leaders can evaluate deployment quality, speed, and cost over time.
Incorporating generative AI into a business requires rigorous data structuring, hard-coded automation, and fine-tuned LLMs, and keeping humans in the loop. By leveraging each technology’s strengths, businesses can create structured facts and context that let LLMs do what they do best. Standardizing efficacy measurements will help businesses evaluate generative AI’s deployment quality, speed, and cost over time.
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