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A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?
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RRitesh
Explanation:
Explanation
Why A is correct:
- Adjusting the prompt is the most direct way to control LLM output characteristics like length and language.
- By modifying the prompt with specific instructions (e.g., "Respond briefly in Spanish"), you can guide the model to produce outputs that match your requirements.
- Prompt engineering is a standard technique for customizing pre-trained LLMs without retraining or changing model architecture.
Why other options are incorrect:
- B. Choose an LLM of a different size: Model size affects capabilities and performance but doesn't directly control output length or language specificity.
- C. Increase the temperature: Temperature controls randomness/creativity in outputs, not length or language.
- D. Increase the Top K value: Top K sampling affects diversity by limiting token selection to top K probabilities, but doesn't control output length or language.
Key Concept: Prompt engineering allows fine-tuning of LLM behavior by providing specific instructions, constraints, and examples in the input prompt.
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