Hello everyone, I've been feeling lately that we need new approaches to interacting with large language models. I'm curious about your experiences in the community to use these models more efficiently. Especially, what strategies work for you in prompt engineering and improving model outputs? What are your general approaches from parameter tuning to output formatting? I'd be happy if you could share your experiences.
Methods to take the rules to the next level
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I make sure to use the "few-shot prompting" method as a simple yet powerful strategy that can be effective in prompt engineering. I prefer this approach especially in cases where complex output formats are required, as it facilitates the model to produce the expected responses. For example, in text summarization tasks, I first strengthen the model's format perception by providing a few examples in the form of "input: [text], output: [summary]". This way, I started to receive consistent and well-structured answers.
In terms of parameter tuning, I balance between diversity and controlled outputs by fine-tuning the `temperature` and `top_p` values. Adjusting these values according to the nature of the question is crucial: for creative content, a high temperature (e.g., 0.8) is beneficial, while for sensitive and repetitive outputs, a low one (e.g., 0.3) is preferred. Additionally, I've found that using output formatting commands along with directives like "Make it shorter/more detailed" also helps improve the outputs.