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ollama.ai | ||
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www.promptingguide.ai
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| | | | | A Comprehensive Overview of Prompt Engineering | |
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tomasvotruba.com
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| | | | | Last week, I kicked off the first post about [tips and tricks with GPT](/blog/lets-share-fails-and-tricks-with-gpt). In the meantime, Marcel posted a great practical piece on [GPT and solutions based on exception messages](https://beyondco.de/blog/ai-powered-error-solutions-for-laravel). Today, we look into 2 pre-trained models that GPT provides - DaVinci and Codex - and how to talk to them to get what we need. | |
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isthisit.nz
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| | | | | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse. | |
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rossmarks.uk
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| | | AI is a hot topic at the moment and I wanted an excuse to play with it and learn how to use OpenAI's API. It is likely that email companies will be using AI to | ||
