[ad_1]
The Potential and Limitations of Generative AI for B2B
Generative AI, including large language models (LLMs), offers unlimited potential for creativity and innovation. However, without a strong anti-hallucination framework, the model can produce false information that can be detrimental to any business. It is imperative to structure data in a way that provides proper context to train highly refined LLMs. In this article, we discuss essential frameworks to incorporate generative AI into your technology stack and get maximum value from it.
Event
Join Top Executives in San Francisco for Transform 2023 conference
Discover how leaders are integrating and optimizing AI investments for success and avoid common pitfalls.
Three Vital Frameworks to Incorporate Generative AI into Your Tech Stack
Build strong anti-hallucination frameworks
While generative AI continues to awe with its creativity, it is often not accurate for B2B requirements. A misfiring generative AI framework can produce misleading and harmful information. Generative AI’s output depends on what the LLM has learned over time and can become irrelevant or problematic if the model is not regularly monitored by humans. That’s why a robust anti-hallucination system is essential to weed out inaccuracies, identify generative falsehoods and improve the model’s accuracy over time.
Orchestrate technology with human checkpoints
Automation can conduct grunt work like aggregating information and coding black-and-white mandates, like return policies. For more complex tasks, generative AI comes in handy. The outputs are advanced and accurate when the inputs are curated before generative AI touches the system. While technology can orchestrate most tasks, human checkpoints are still crucial to correcting errors and verifying model output accuracy.
Measure outcomes via transparency
LLMs are black boxes that function on a preferred set of inputs. The industry lacks standard efficacy measurements. Currently, there are companies bringing clarity across generative AI models, linking data back to customer feedback, and evaluating deployment quality, speed, and cost over time. This standardization can be an excellent way to measure outcomes via transparency.
Conclusion
Generative AI offers unparalleled potential to businesses that want to improve workflows and processes. However, the models have a few limitations as described. Incorporating generative AI into your technology stack requires a viable model, a well-structured data framework, a robust anti-hallucination system, human checkpoints, and transparency measures to measure outcomes effectively.
FAQs
What is generative AI?
Generative AI is an advanced AI model that produces human-like responses with creative inputs.
What are the limitations of generative AI?
Generative AI’s limitation is its inherent inability to deliver accurate, context-specific information oriented toward a particular task without proper and consistent monitoring by humans.
Is generative AI useful for B2B businesses?
Yes, generative AI is useful for B2B businesses, but its outputs must be well-managed and properly structured and monitored.
[ad_2]
For more information, please refer this link