Generative AI is a type of artificial intelligence that is used to create new content, including conversations, stories, images, videos, and music. It operates on the basis of large machine learning models, such as Foundation Models (FM) and Large Language Models (LLM), which are trained on vast amounts of generalized and unlabeled data. These models can perform a wide range of tasks with a high level of accuracy based on input prompts.
While pre-trained models like LLM can continue learning from new data inputs during inference, they may struggle with complex tasks. One technique to address this is prompt chaining, where a complex task is broken down into smaller subtasks for better handling. However, even though generative AI can produce highly realistic content, it may also generate results that are plausible but factually incorrect. It is essential to incorporate human judgment in decision-making processes, especially in complex and high-risk scenarios.
In a retail example discussed in a blog post, an automated system responds to customer reviews using generative AI. If the generated response contains uncertainty regarding toxicity or tone, a human reviewer is engaged to make the final decision. This process is facilitated by an event-driven architecture, where events trigger workflows that include generative AI content generation and human decision-making.
Prompt chaining simplifies tasks for LLM by breaking them down into manageable subtasks, leading to more consistent and accurate responses. Step Functions, an orchestration tool, is ideal for implementing prompt chaining as it allows tasks to be executed sequentially, in parallel, and iteratively.
Human-in-the-loop decision-making involves human review to enhance system accuracy in situations where toxicity levels are unclear. This can be integrated into Step Functions workflows using callback integrations with AWS Lambda functions. Architecting based on event-driven systems helps build scalable architectures that can evolve by adding new consumers subscribing to events without changing existing code.
Overall, the blog post emphasizes the importance of combining generative AI techniques like prompt chaining with human oversight to improve accuracy and safety. It also highlights the benefits of event-driven architectures and workflow orchestration in integrating generative AI applications with existing systems. For further examples and workflows utilizing Step Functions, readers are encouraged to visit Serverless Land for more resources.
The authors of the blog post, Veda Raman and Uma Ramadoss, are seasoned AWS architects with expertise in generative AI, machine learning, serverless services, and event-based architectures. They work with clients to design efficient, secure, and scalable machine learning applications tailored to their needs.
Article Source
https://aws.amazon.com/blogs/machine-learning/building-generative-ai-prompt-chaining-workflows-with-human-in-the-loop/
