Most prompt engineering techniques help AI think better. ReAct helps AI think AND do.
Chain-of-thought gets you better reasoning. Few-shot gets you consistent formatting. But what about tasks that require the AI to research, verify, iterate, and act?
That’s where ReAct comes in.
What is ReAct?
ReAct stands for Reasoning + Acting. It’s a prompting pattern where the AI alternates between three states:
- Thought: What do I know? What’s missing? What should I do next?
- Action: Execute a concrete step
- Observation: What did I learn? Does it change my approach?
Then it loops back until the task is complete. This is fundamentally different from standard prompt-and-respond.
Why ReAct matters
Real tasks aren’t one-shot. “Analyze the competitive landscape” requires research. “Debug this error” requires investigation. “Plan this project” requires evaluating options.
ReAct is the foundation of AI agents — systems that can autonomously complete complex, multi-step work.
A practical example
Task: “Find the most cost-effective LLM for customer support ticket classification.”
Without ReAct: The AI generates an answer from training data. Might be outdated.
With ReAct:
Thought 1: I need to identify candidate models for classification tasks.
Action 1: List candidates with known classification strengths.
Observation 1: Six viable candidates. Smaller models might outperform on cost-efficiency.
Thought 2: Estimate cost per classification for each model.
Action 2: Calculate based on ~200 input, ~50 output tokens per classification.
Observation 2: Llama 3 and Mistral are cheapest per classification. But accuracy matters.
Thought 3: Factor in retry rates and misclassification costs.
Action 3: Model total cost per correctly classified ticket.
Observation 3: Claude and GPT-4 have lowest effective cost despite higher per-token pricing.
When to use ReAct
Use when: Multi-step investigation, verifying information, decisions requiring tradeoffs.
Skip when: Simple generation, straightforward tasks, tight token budgets.
How different models handle ReAct
- Claude: Excels at structured, multi-step reasoning
- GPT-4: Strong but tends to take shortcuts
- Gemini: Good at connecting observations to broader context
- DeepSeek: Strong on technical reasoning, especially code debugging
PrismForge’s Prompt Builder includes ReAct as one of 13 built-in techniques. Toggle it on and test across models.
Engineered prompts outperform raw prompts. ReAct is where prompt engineering meets real-world problem solving.