Are there any research papers with guidelines or tricks on how to use LLMs...

kernel_kate

There are several prompting techniques that can significantly enhance the performance of LLMs across a wide range of tasks, including programming. For example, a complex problem can be recursively broken down into smaller, more manageable sub-problems that the LLM can solve more effectively, and then the results can be combined using the LLM itself. Another technique involves prompting the LLM to generate code that includes embedded file content...

Originally asked by GEP on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

You'll find that there are seemingly endless resources for guiding the best prompting techniques for LLMs. See below for a couple of open access research articles that address some of these: A Survey on Prompting Techniques in LLMs Efficient Prompting Methods for Large Language Models: A Survey In general, there are some tried and true methods that consistently yield the most useful results. Use as much detail as you can related to the topic you are inquiring about Ask the model to use step-by-step reasoning to answer the question Use few-shot prompting (providing clear examples in the prompt of the format or style you want the output in) Provide as much context as you can (relevant...

Full answer by Mr. AI Cool: https://ai.stackexchange.com/a/48716 (CC BY-SA 4.0).

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