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AIF-C01 · Question #250

A grocery store wants to create a chatbot to help customers find products in the store. The chatbot must check the inventory in real time and provide the product location in the store. Which prompt…

For a chatbot that needs to check real-time inventory and provide product locations, Reasoning and Acting (ReAct) prompting is the appropriate technique.

Submitted by rania.sa· Mar 30, 2026Applications of Foundation Models

Question

A grocery store wants to create a chatbot to help customers find products in the store. The chatbot must check the inventory in real time and provide the product location in the store. Which prompt engineering technique should the store use to build the chatbot?

Options

  • AZero-shot prompting
  • BFew-shot prompting
  • CLeast-to-most prompting
  • DReasoning and acting (ReAct) prompting

Why each option

For a chatbot that needs to check real-time inventory and provide product locations, Reasoning and Acting (ReAct) prompting is the appropriate technique.

AZero-shot prompting

Zero-shot prompting relies solely on the LLM's pre-trained knowledge without any examples or external tools, which is insufficient for real-time inventory checks and location lookups.

BFew-shot prompting

Few-shot prompting provides a few examples in the prompt to guide the model's response style or format but does not inherently equip it with the ability to perform external actions or retrieve real-time data.

CLeast-to-most prompting

Least-to-most prompting is a technique for breaking down complex problems into a series of simpler subproblems, which helps with reasoning but doesn't inherently facilitate external tool use for real-time data retrieval.

DReasoning and acting (ReAct) prompting

Concept tested: Prompt engineering for external tool use

Source: https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html

Topics

#ReAct prompting#prompt engineering#real-time data#AI agents

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