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Related Questions
- Can you elaborate on the importance of tokenizer choice in LLM design?
- What are some best practices for crafting context-free prompts that elicit meaningful responses from LLMs?
- How do you account for common pitfalls, such as answer extraction from training data or overreliance on surface-level patterns, in LLM prompt design?
- Can you discuss the role of input formatting and structuring in influencing LLM output?
- How do you strike a balance between providing enough context and overwhelming the model with extraneous information?
- What strategies can be employed to increase the generalizability of LLM models to out-of-domain prompts?
- Can you walk me through the process of evaluating the performance and understanding the limitations of LLM models on a given prompt?
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