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Tell the model what NOT to do in addition to what you want. For example, 'Explain quantum computing without using analogies or metaphors.' Negative constraints often produce more focused and accurate outputs than positive instructions alone.

For summarization tasks, shorter context windows help LLMs focus on key points.

Challenge: Find a complex technical article (e.g., from arXiv). Ask an LLM to summarize it for three different audiences: a 10-year-old child, a university undergraduate, and a PhD-level expert in the field. Compare the differences in language, detail, and focus.

Control LLM output creativity using 'temperature' (randomness) and 'top_p' (nucleus sampling). Lower temperature means more deterministic, focused output. Higher values increase creativity/randomness.

The 'temperature' parameter controls randomness. Lower values (e.g., 0.2) make output more deterministic and focused. Higher values (e.g., 0.8) increase creativity and diversity. Adjust it based on your task.

Adding constraints to your prompt (e.g., word count, specific keywords to include/exclude, format requirements) can often lead to more focused and useful outputs from the LLM.

The attention mechanism in transformers allows models to focus on relevant parts of the input when generating each token, enabling better understanding of long-range dependencies in text.