Insider Tips To Successful AI Implementation

In the rapidly evolving field of artificial intelligence, it's crucial to understand how to optimise the performance of AI models. This guide aims to provide you with strategies and tactics to get the most out of AI models, specifically focusing on Generative Pretrained Transformers (GPTs).

1. Crafting Clear Instructions: AI models aren't mind readers. The clearer your instructions, the more accurate the output. If you want brief replies, ask for them. If you need expert-level writing, specify it. The less the AI has to guess, the better the results. Tactics include providing detailed queries, asking the model to adopt a persona, using delimiters to distinguish parts of the input, outlining the steps required to complete a task, providing examples, and specifying the desired length of the output.
2. Providing Reference Text: AI models can sometimes generate inaccurate answers, especially when asked about obscure topics or for citations and URLs. Providing reference text can help the AI generate more accurate responses. Tactics include instructing the model to answer using a reference text or to answer with citations from a reference text.
3. Breaking Down Complex Tasks: Just like in software engineering, complex tasks can be broken down into simpler subtasks. This can reduce error rates and make the task more manageable for the AI. Tactics include using intent classification to identify the most relevant instructions for a user query, summarising or filtering previous dialogue for long conversations, and summarising long documents piecewise and constructing a full summary recursively.
4. Allowing Time for "Thinking": AI models, like humans, can benefit from having time to "think" or process information. This can reduce reasoning errors and improve the quality of the output. Tactics include instructing the model to work out its own solution before rushing to a conclusion, using inner monologue or a sequence of queries to guide the model's reasoning process, and asking the model if it missed anything on previous passes.
5. Leveraging External Tools: To compensate for the weaknesses of AI models, you can use the outputs of other tools. For instance, a text retrieval system can provide relevant documents, and a code execution engine can help with maths and code execution. Tactics include using embeddings-based search for efficient knowledge retrieval and using code execution for more accurate calculations.
6. Testing Changes Systematically: To ensure that a change improves performance, it's necessary to test it systematically. A comprehensive test suite can help determine if a modification improves performance on a representative set of examples.

Remember, these strategies and tactics are not exhaustive, and you should feel free to experiment and try creative ideas not represented here. The goal is to enhance the performance of AI models and get the most out of them.

Feel free to DM me to ask any questions about using AI to help you be more productive... Or BOOK A CALL to see how I can help you use AI successfully in your life!