What is prompt engineering?
Why D is the answer
Designing inputs to get better model output
Prompts shape what the model produces.
In detail
- Prompt engineering designs inputs to get better output.
- Iterate on prompts as you would on a draft.
Use case
An illustrative scenario, not a real incident.
Adding a clear role, format and example to a prompt turns a vague answer into a useful one.
See it in different ways
flowchart LR A["Zero-shot"] --> B["One-shot"] B --> C["Few-shot"] C --> D["Prompt chaining"] D --> E["Chain of thought and ReAct"]
mindmap
root((Prompting))
Examples
Zero-shot
One-shot
Few-shot
Persona
Role prompting
Steps
Prompt chaining
Tools
ReActflowchart LR S0["Write prompt"] --> S1["Review output"] --> S2["Refine"] --> S3["Save it"]
Key point: Prompts shape output.
Why the other options are wrong
- A. Building physical chips for AI models
- That is hardware.
- B. Writing the terms of a software license
- That is legal work.
- C. Designing the network for a data center
- That is networking.