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Do Prompt Structures Improve Output Quality? Testing Prompts with GPT-4, Claude 3 and Gemini 1.5

๐ŸŒˆ Abstract

The article explores the effectiveness of using different prompt structures to improve the output quality of large language models (LLMs) like GPT-4, Claude 3, and Gemini 1.5 for a specific task - analyzing meeting transcripts and providing criteria-based evaluations. It compares the performance of four prompts with varying levels of structure and detail.

๐Ÿ™‹ Q&A

[01] Prompts to Study

1. What are the different versions of the prompt for analyzing daily meeting transcripts? The article presents four versions of the prompt:

  • Brief Prompt: A short prompt that describes the task without using any prompt engineering techniques.
  • Unstructured Detailed Prompt: A more detailed prompt that provides the same information as the structured version but without any formatting or structure.
  • Structured Detailed Prompt: A detailed prompt that uses Markdown formatting (headings and lists) to structure the information.
  • Step-by-Step Detailed Prompt: A prompt that breaks down the task into explicit steps.

2. How do the prompts differ in terms of length and complexity? The prompts vary in length, from 210 tokens for the Brief Prompt to 520 tokens for the Step-by-Step Detailed Prompt. The more detailed prompts add structure and step-by-step instructions, but the content remains the same across all versions.

3. What is the purpose of comparing these different prompt versions? The article aims to explore whether using more structured and detailed prompts can improve the output quality of LLMs for the specific task of meeting transcript analysis and evaluation. The goal is to determine if the additional effort required to craft longer, more complex prompts is justified by the resulting output quality.

[02] Experiment Description

1. What are the key aspects of the experiment design? The experiment tests the four prompt versions on three different meeting transcripts, with varying lengths and quality. The input data also includes specific instructions, such as evaluating using all criteria or only a subset, and mentioning participant names in the explanations.

2. How is the output quality measured? The output quality is evaluated based on the "number of defects" in the LLM's response, such as missing information, incorrect formatting, or not following the prompt instructions.

3. What are the limitations of the experiment's approach? The article acknowledges that with a small number of runs, it is difficult to draw statistically significant conclusions about the differences between prompts. The goal is to provide a qualitative assessment of whether increasing prompt size and complexity is worthwhile.

[03] Experiment Results: Prompt Comparison

1. What are the key findings from the experiments? The most notable finding is that the Brief Prompt (the shortest version) performed just as well as the more detailed prompts, with an average of only 4.8 defects across the tested models (excluding GPT-3.5). The structured and unstructured long prompts also showed no significant differences in performance.

2. How do the different models perform in the experiments? The article highlights that GPT-4 and Claude 3 Opus performed the best, with the latter being particularly adept at following detailed prompts. Gemini 1.5 Pro had some unique strengths, such as providing more specific explanations about the meeting events.

3. What are the implications of the results for choosing the right model for a task? The article suggests that the experiment results can help users make more informed decisions when selecting a model for different tasks, based on their specific needs and preferences (e.g., following prompts exactly, extracting specific facts, or inferring user needs).

[04] A Few Words on Choosing the Right Model for a Task

1. What are the key points regarding the general quality of the tested models? The article cautions against drawing conclusions about the overall quality of the models based on this simple experiment, as comprehensive LLM benchmarks are better suited for such assessments. The focus should be on understanding the specific strengths and weaknesses of each model for the given task.

2. What insights does the article provide on selecting the right model for a task? The article suggests that users can make more informed decisions about model selection by understanding the particular defects or behaviors exhibited by each model in the experiments. For example, Claude 3 Opus may be preferred if the task requires following detailed prompts, while GPT-4 may be better suited for tasks where the user doesn't have time to describe their needs in detail.

[05] Conclusion

1. What are the key takeaways from the article's conclusion? The main takeaways are:

  • Using a brief prompt can often produce outputs of similar quality to more structured and detailed prompts, suggesting that extensive prompt engineering may not always be necessary.
  • Adding step-by-step instructions to prompts can be risky, as LLMs may not handle them well and may start writing steps to the user, which can appear odd.
  • The best-performing models, such as GPT-4 and Claude 3 Opus, are able to handle larger, more detailed prompts well, but this should not be relied upon as a quality improvement strategy.
  • The quantitative results of the article are specific to the "transcript analysis by criteria" task and may not generalize to other types of tasks.

2. What advice does the article provide for AI users regarding prompt engineering techniques? The article suggests that AI users should focus on understanding the general advice on which prompt engineering techniques bring value in various situations, as this can help them save time by avoiding unnecessary techniques. The article provides such general advice based on the experiments conducted.

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