ReAct: Teaching LLMs to Think, Act, and Observe
ReAct teaches an LLM to solve problems by interleaving thought, action, and observation. This is key for agents that search the web or query APIs to answer questions with external data.
ReAct gives LLMs a powerful problem-solving loop: think, act, observe. Instead of just reasoning internally, it generates a thought, executes an action (like a tool call), and analyzes the resulting observation to inform its next thought. This is the foundation for agents that search the web for current events or query internal databases. The common mistake is thinking ReAct is just complex prompting; its power comes from the 'Act' step grounding the model in an external environment.
Read the original → research.google
- #llm
- #generative ai
- #prompting
- #agent
Get five bites like this every day.
Tezvyn delivers a daily feed of 60-second tech bites with quizzes to lock in what you learn.