Hello, I'm planning to optimize some functions in a recent project using a small LLM. What methods do people in this field usually prefer? For example, the biggest challenge with prompt engineering is getting consistent, high-quality output from the given inputs. What are the general approaches to this? Detailed benchmark or testing strategy advice would also be helpful.
What methods should be tried when developing projects with LLMs?
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One of the most important tricks to ensure consistency in prompt engineering is **system messages**. For example, clearly defining the model's behavior like "You are a helpful assistant optimized for concise answers in German" works wonders. I use this in my project trials and have seen a serious improvement in output quality.
Another approach is **few-shot examples**. For instance, I provide 3-4 examples upfront in the desired output style ("For the given input, provide the output in this format:..."). This way, I guide the model in terms of both format and content. For benchmarking, I save the outputs of previous trials in a CSV file and compare them using Python evaluation libraries like RAGAS—this makes it easy to measure which parameter settings improve consistency.