Wen LL@wdli

In the rapidly progressing world of large language models (LLMs) like GPT-4, the art of crafting effective prompts is crucial for harnessing their potential. However, the dynamic nature of LLM research presents a challenge: keeping up with the continuous influx of new prompting techniques. This is where our project, the "Prompt Consultant", steps in. The Prompt Consultant aims to guide users in generating more effective prompts, not by having them chase the latest research, but by leveraging the power of the LLMs themselves. We exploit the LLM’s capacity to assimilate the best prompting resources and provide insights for improving prompts. The challenge is that LLMs, due to their static training data, are not aware of the latest prompting tricks. Our solution is to use in-context learning, incorporating the most recent prompting resources directly into the prompt. Anthropic's long-context API is a critical component of this endeavor. It's impractical to train a new model every time a new prompting method emerges, and the versatility of user queries makes common vector search methods insufficient. The long-context API allows us to include extensive relevant prompting information directly in the context. Our proof-of-concept demo uses the latest resources from learnprompting.org, embedding them in the model’s context. Users can then consult our bot, implemented on Anthropic’s Claude-v1.3-100k model, to enhance their prompts. Our preliminary results show promise, indicating LLMs' potential to stay in step with rapid advancements in their field. In essence, the Prompt Consultant bridges the gap between the rapid progression of LLM research and practical, effective usage of these models. By leveraging the LLMs themselves, we aim to make these technologies more accessible, democratizing the benefits of AI research. Our project foresees a future where anyone, regardless of their expertise, can generate high-quality outputs from these models through optimized prompting.

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