Non-Obvious Prompt Engineering Guide

the article by Adam Gospodarczyk discusses advanced techniques in prompt engineering for large language models (LLMs). It emphasizes the autoregressive nature of LLMs, where each generated token influences subsequent tokens, making it crucial to provide well-structured prompts. The author suggests that effective prompt engineering involves steering the model's behavior by carefully selecting the tokens provided in user messages and system prompts. Techniques such as breaking down complex problems into smaller steps, using clear separators for different sections of prompts, and providing diverse examples are highlighted as essential strategies. The article also mentions the importance of activating specific areas of the model's latent space by using relevant terminology and concepts. Gospodarczyk advises against overwhelming the model with excessive information, advocating for clarity and precision in prompts. He introduces the idea of using meta-prompts to collaboratively develop and refine prompts with the LLM, enhancing the overall effectiveness of the interaction. The conclusion stresses the significance of understanding the token prediction process in LLMs, which is fundamental for successful prompt engineering. Overall, the guide provides unique insights into optimizing interactions with LLMs, focusing on the interplay between user input and model output to achieve desired results.