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A data scientist is designing a prompt for an Azure OpenAI Service model to summarize lengthy technical documents. The summaries need to be concise, highlight key findings, and be suitable for a non-technical audience. Which prompt engineering technique would be most effective to ensure the model focuses on these specific requirements?
- AZero-shot prompting
- BInstruction-based prompting
- CChain-of-thought prompting
- DFew-shot prompting
Show answer & explanationAnswer & explanation
Correct answer: B. Instruction-based prompting
Instruction-based prompting involves providing explicit, detailed instructions within the prompt to guide the model's output. For summarization with specific requirements like conciseness, key findings, and audience suitability, clear instructions are crucial.
Why the other options are wrong
- A. Zero-shot prompting relies on the model's inherent understanding without examples, which might not consistently meet specific formatting/content requirements.
- C. Chain-of-thought prompting encourages step-by-step reasoning, which is less relevant for direct summarization output requirements than explicit instructions.
- D. Few-shot prompting provides examples, but direct instructions are often more effective for defining output format and content constraints for summarization.
Instruction-based Prompting
A prompt engineering technique where explicit, detailed instructions are provided to guide the large language model (LLM) to produce a desired output format, content, or style.
- Involves clear, direct commands within the prompt.
- Effective for enforcing specific output constraints.
- Reduces ambiguity and improves consistency of results.
Memory trick: Instructions are like a 'GPS for the GPT' guiding its output precisely.