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I got early access to ChatGPT API and then pushed it to it’s limits. Here’s what you need to know.

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Summary

This article discusses the use of the ChatGPT API and how it differs from the davinci-003 LLM. It breaks down the components of the API, such as the system prompt, subroutines, memory editing, and context window management. It also offers implementation tips and encourages readers to check out the Buildt code search tool on the VSCode extension marketplace.

Q&As

What are the differences between the ChatGPT API and davinci-003?
The ChatGPT API works slightly differently from the standard playground models, as it does not require the use of special tokens to wrap messages between the user and the chatbot. ChatGPT prompts include some bot personality information, and are more 'zero shot' than davinci prompts, which are more specialised 'functions'. The ChatGPT API is also 10x cheaper than davinci-003.

What is the purpose of the system prompt in ChatGPT?
The system prompt is the bot's 'brief' which defines its character, purpose and available actions. It is sent with every request, and its weight/influence on the output wanes the longer the context window becomes.

How can subroutines be used with ChatGPT to perform actions outside of its sandbox?
Subroutines can be used by using special tags/tokens to represent different sequences. The API will tell when it has stopped due to a stop sequence, and the request can be parsed. The results can then be passed into the context window, and either the turn can be ended or the conversation can be left open-ended.

How can user intent be identified within ChatGPT conversations?
User intent can be identified by adding further tags which must be written by the ChatBot before it writes any user-facing text. This forces the model to verbalise its reasoning and pre-meditate its actual answer before delivering it.

What are some tips for managing the context window within ChatGPT?
Tips for managing the context window include ensuring that the system prompt is always included in each request, and considering if the user will want to go back in time to where they started when pruning early messages. It is also important to have a strong function to strip out content that the user shouldn't see from the chat messages.

AI Comments

👍 Alistair Pullen's detailed article on ChatGPT API is an insightful and comprehensive guide on how to make the most out of this powerful new technology!

👎 The article by Alistair Pullen on ChatGPT API is overly long and technical, making it difficult to follow and understand.

AI Discussion

Me: It's about using ChatGPT API in order to add chat functionality to code. It goes into the differences between ChatGPT and davinci-003, how to use the System Prompt, the importance of subroutines and memory editing, and implementation tips.

Friend: Wow, that sounds really interesting. What implications does this have?

Me: Well, the article talks about using ChatGPT API to add chat functionality to code, which is a powerful tool for developers. It also suggests that it's 10x cheaper than davinci-003, so it could be a great cost-saving measure. Furthermore, the article touches on the importance of subroutines and memory editing, which are important for creating a more efficient and effective chatbot. Finally, it provides some implementation tips that developers can use when integrating ChatGPT into their code. All of this shows that ChatGPT API has the potential to be a powerful and cost-effective tool for developers.

Action items

Research the differences between ChatGPT and davinci-003.
Implement a system prompt for a chatbot that includes bot personality information.
Develop a method for allowing a chatbot to perform actions, insert those actions into the context window, and then continue.

Technical terms

API: Application Programming Interface. A set of tools, protocols, and routines for building software applications.
YC: Y Combinator. A startup accelerator that provides seed funding, advice, and connections to entrepreneurs.
LLM: Language Model. A type of artificial intelligence that uses natural language processing to generate human-like text.
k-shot: A type of language model that requires a few examples of the desired output before it can generate its own output.
React/JSX: A JavaScript library for building user interfaces.
Subroutine: A set of instructions that can be used to perform a specific task.
Semantic Codebase Search Engine: A type of search engine that uses natural language processing to search through a codebase.
Regular Expressions: A sequence of characters that define a search pattern.
Enum: A data type that consists of a set of named values.