Practical Prompt Engineering 1: Determining the Optimal Number of Few-Shot Samples Through Experimentation
Published:Dec 25, 2025 01:40
•1 min read
•Qiita LLM
Analysis
This article introduces prompt engineering as a method to improve the accuracy of LLMs by refining the prompts given to them, rather than modifying the LLMs themselves. It focuses on the Few-Shot learning technique within prompt engineering. The article likely explores how to experimentally determine the optimal number of examples to include in a Few-Shot prompt to achieve the best performance from the LLM. It's a practical guide, suggesting a hands-on approach to optimizing prompts for specific tasks. The title indicates that this is the first in a series, suggesting further exploration of prompt engineering techniques.
Key Takeaways
Reference
“LLMの精度を高める方法の一つとして「プロンプトエンジニアリング」があります。(One way to improve the accuracy of LLMs is "prompt engineering.")”