Using GPT-4 effectively remains crucial despite the rapid advancements in AI models. Recent research suggests that concise prompts provide better results. This article explores ways to use GPT-4 more effectively.
Why Concise Prompts Are Important
Since GPT has already learned a vast amount of common knowledge, concise prompts can yield more accurate results. For example, instead of listing states, simply requesting state names can produce better results. This prevents the model from becoming confused by unnecessary information.
Powerful Features of OpenAI Chat API
The OpenAI Chat API alone can implement robust functionalities. Necessary functions like JSON extraction can be implemented easily without additional tools like Langchain. Moreover, upgrading the GPT model requires only a simple change of a string in the codebase.
Error Handling and Input Length Limits
When using the OpenAI API, error handling and input length limits must be considered. Handling cases where GPT finds nothing can be challenging. Solutions include returning an empty value in the prompt or not sending a prompt when the input is empty.
Output Limitations of GPT
While GPT-4’s input limit is 128k tokens, its output limit is only 4k tokens. This means it is challenging to reliably generate more than ten items in a JSON object list. Therefore, designing prompts with output length in mind is necessary.
Limitations of Vector Databases and RAG/Embeddings
For general purposes, vector databases and RAG (embeddings) may not be very helpful. RAG does not work well for purposes other than search and has practical issues such as relevance determination, data isolation problems, and decreased user satisfaction. Hence, GPT is more suitable for complex query generation or facet searches.
GPT’s Hallucination Problem
GPT provides highly reliable results when extracting information from given texts. However, it can hallucinate if insufficient context is provided. Therefore, it is crucial to provide enough information and handle GPT’s responses well when designing prompts.
Future Outlook
Optimizing and utilizing GPT-4 seems to be a realistic choice for now. Achieving AGI (Artificial General Intelligence) may be difficult with just transformer models, web data, and large-scale infrastructure. Therefore, even if GPT-5’s performance is not revolutionary compared to GPT-4, focusing on optimal prompt design and application remains important.
Reference: kenkantzer, “Lessons after a half-billion GPT tokens”