What is ReAct prompting?
PICTURE THIS: AN LLM TURN
Simple meaning
ReAct interleaves reasoning traces with actions such as search or calculator calls.
WHY — Tokens instead of words?
LLMs use tokens (not full words) because it helps them:
who only read docs from people who shipped.
and tied to GenAI / LLM work.
Each piece maps to a number the network can learn.
Fixed pieces are what transformers expect as input.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1ReAct interleaves reasoning traces
with actions such as search or calculator calls.
- 2The model thinks, acts,
observes, and repeats until it can answer.
- 3It is a prompting
pattern behind many simple agents.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
ReAct interleaves reasoning traces with actions such as search or calculator calls. The model thinks, acts, observes, and repeats until it can answer.