Andrew Ng has come up with four agentic workflow design patterns enhancing AI’s capability: Reflection, Tool use, Planning, and Multi-agent collaboration.
In the Reflection design pattern, Andrew Ng introduces a method where AI systems, particularly Large Language Models (LLMs), enhance their outputs through self-reflection. This involves the AI critiquing its initial output and using this critique to produce a more refined response. This iterative process of generating, critiquing, and improving not only elevates the quality of outcomes but also applies to a range of tasks including coding, writing, and answering questions. Additionally, the article touches on the potential of multi-agent collaboration and tool use in further improving AI-generated content.
Why Should You Care?
Generative AI design patterns like Reflection are important for advancing AI and automation because:
– Reflection allows AI models to automatically criticize and improve their own output.
– It enables self-reflection and iterative refinement in tasks like writing code and answering questions.
– Using tools and feedback, models can identify errors and generate ideas for improvement.
– Implementing Reflection in a multi-agent framework leads to improved responses.
– These design patterns have shown surprising performance gains and can be implemented quickly.
– Reflection can be applied to various tasks, enhancing the quality of AI-generated output.
– By automating critical feedback, models can continuously refine their responses.