ReAct Prompting
All that was mentioned is possible due to the fact that agenticĀ AIĀ uses Ā chain-of-thoughtĀ (CoT) reasoning to improve its ability to perform complex, multi-step tasks autonomously.Ā CoTĀ is a prompt-engineering method designed to improve the reasoning capabilities of Large Language Models (LLMS), especially for tasks that require complex, multi-step thinking.
Chain-of-thought (CoT) prompting demonstrated that large language models can generate explicit reasoning traces to solve tasks requiring arithmetic, logic, and common-sense reasoning. However,Ā CoTĀ has a critical limitation: because it operates in isolation, without access to external knowledge or tools, it often suffers from fact hallucination, outdated knowledge, and error propagation.
ReAct (Reason + Act) addresses this limitation by unifying reasoning and acting within the same framework. Instead of producing only an answer or a reasoning trace, a ReAct-enabled LLM alternates between:
- Verbal reasoning traces: Articulating its current thought process.
- Actions: Executing operations in an external environment (e.g., searching Wikipedia, querying anĀ API, or running code).
This allows the model to:
- Dynamically plan and adapt: Updating its strategy as new observations come in.
- Ground reasoning in reality: Pulling in external knowledge to reduce hallucinations.
- Close the loop between thought and action: Much like humans, who reason about what to do, act, observe the outcome, and refine their next steps.