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ReAct Prompting: Quickly Generate Context-Aware Questions

ReAct Prompting: Quickly Generate Context-Aware Questions

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Have you ever found yourself frustrated with the limitations of traditional prompting methods? You're not alone. Traditional prompts often lack the ability to understand context or take action based on the information they gather. Enter ReAct Prompting, a groundbreaking technique that's setting new standards in the world of intelligent prompting.

In this comprehensive guide, we'll delve into the nuts and bolts of ReAct Prompting. From its mechanics to its applications, we'll explore why this innovative approach is making waves in various industries. Buckle up; you're in for an enlightening ride.

The Mechanics of ReAct

What is ReAct Prompting?

ReAct Prompting is not your average prompting technique. It's a sophisticated method that combines reasoning and action to create smarter, context-aware prompts. Unlike traditional methods that merely ask questions, ReAct goes a step further. It understands the context, reasons out the best course of action, and then performs tasks based on that reasoning.

  • Reasoning: The system understands the context of the question and what needs to be done.
  • Action: It then performs specific tasks like calculations or data retrieval based on that understanding.

For example, let's say you ask, "What's the square root of the age of the oldest person in the room?" A ReAct prompt would first identify the oldest person, find their age, and then calculate the square root. All in one go!

How Does ReAct Work?

The magic behind ReAct lies in its use of agents. These are specialized software components designed to perform tasks based on the prompts they receive. Here's a step-by-step breakdown:

  1. Receive Prompt: The agent receives a prompt from the user.
  2. Analyze Context: It then analyzes the context and breaks down the prompt into actionable tasks.
  3. Perform Tasks: The agent performs the tasks, such as data retrieval or calculations.
  4. Generate Response: Finally, it generates a response based on the tasks performed.
# Sample code to demonstrate a simple ReAct agent
def react_agent(prompt):
    # Analyze context
    context = analyze_context(prompt)
    # Perform tasks based on context
    tasks = perform_tasks(context)
    # Generate response
    response = generate_response(tasks)
    return response

By using agents, ReAct achieves a level of flexibility and intelligence that's unparalleled in traditional prompting systems. It's like having a mini-assistant that not only understands what you're asking but also knows how to get it done.

The Role of Agents in ReAct

Agents are the backbone of any ReAct system. They are programmed to understand the intricacies of language, context, and action. These agents can range from simple rule-based systems to complex machine learning models. The more advanced the agent, the more tasks it can perform.

  • Data Retrieval: Agents can pull data from various sources to answer queries.
  • Calculations: They can perform complex calculations on the fly.
  • Context Awareness: Advanced agents can even understand the context in which a question is asked, making the responses more accurate and relevant.

Certainly, let's dive into the complete code examples for both react-LLM and LangChain React.

react-LLM: A Complete Guide to Running LLMs in the Browser

What is react-LLM?

react-LLM is a library that allows you to run Language Learning Models (LLMs) directly in your browser. It leverages React Hooks and WebGPU to provide a seamless and efficient experience.

Check out its GitHub Page here (opens in a new tab).

How to Use react-LLM


First, install the necessary package:

npm install @react-llm/headless

Initialization and Configuration

Initialize and configure the model using the ModelProvider:

import { ModelProvider } from "@react-llm/headless";
export default function Home() {
  return (
        kvConfig: {
          numLayers: 64,
          shape: [32, 32, 128],
          dtype: 'float32',
        wasmUrl: 'https://your-custom-url.com/model.wasm',
        // ...other configurations
      <Chat />

Using the Hooks

Here's how to use the useLLM hook:

import useLLM from '@react-llm/headless';
const MyComponent = () => {
  const {
    // ...other functionalities
  } = useLLM();
  // Your component logic here
  return null;

How to Use React with LangChain


First, install the LangChain package:

pip install langchain

Initialization and Configuration

Initialize the language model and load the tools:

from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)

Creating an Agent

Here's how to create an agent using LangChain:

from langchain.agents import AgentExecutor
from langchain.agents import AgentType
agent_executor = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_executor.invoke({"input": "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"})

These code examples should give you a comprehensive understanding of how to implement react-LLM and LangChain React in your projects.


We've explored the depths of ReAct LLM and LangChain React, two powerful frameworks for implementing Language Learning Models (LLMs) and ReAct logic, respectively. Whether you're a React developer looking to run LLMs in the browser or a Python developer aiming to integrate ReAct logic into your applications, these frameworks offer robust, efficient, and customizable solutions. With features like WebGPU acceleration, persistent storage, and a plethora of customization options, ReAct LLM and LangChain React are must-haves for anyone serious about leveraging the power of LLMs and ReAct in their projects.


What is ReAct prompting?

ReAct prompting is a method of using agents to implement logic that involves both reasoning and action. It's a way to make your language models more interactive and dynamic, allowing them to not just generate text but also perform tasks based on the prompts they receive.

Is ReAct the same as chain-of-thought prompting?

No, ReAct is not the same as chain-of-thought prompting. While chain-of-thought prompting focuses on generating a sequence of related ideas or thoughts, ReAct involves both reasoning and action. It's more dynamic and interactive, allowing the model to perform tasks based on the prompts.

What is reason and action ReAct?

Reason and action in ReAct refer to the two main components of the logic. "Reason" involves the model's ability to understand and process the prompt, while "action" involves the steps the model takes based on that understanding. For example, if the prompt is to find information and then perform a calculation, the "reason" part would involve understanding the information needed, and the "action" part would involve actually finding that information and performing the calculation.

Can you use langchain with ReAct?

Yes, you can use LangChain with ReAct. LangChain provides a set of tools and utilities that make it easier to implement ReAct logic in your applications. It's particularly useful for Python developers and offers a straightforward way to create ReAct agents.

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