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You are here		blog.finxter.com Are LLMs Greedy? An Experimental Analysis of the Tipping Prompt ($0 to $1 Million) - Be on the Right Side of Change
|	|	weisser-zwerg.dev Running AI Models on Your PC: A Guide to Local Large Language Models (LLMs)	3.2 parsecs away Travel
|	|	Setting Up AI Models on Older Hardware - A Beginner's Guide to Running Local LLMs with Limited Resources
|	|	www.promptingguide.ai Jailbreaking LLMs | Prompt Engineering Guide	1.4 parsecs away Travel
|	|	A Comprehensive Overview of Prompt Engineering
|	|	isthisit.nz Prompting GPT Models to Return JSON	2.8 parsecs away Travel
|	|	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.
|	|	wtfleming.github.io Cats vs Dogs - Part 3 - 99.1% Accuracy - Binary Image Classification with PyTorch and an Ensemble of ResNet Models	13.0 parsecs away Travel
|		[AI summary] This post discusses achieving 99.1% accuracy in binary image classification of cats and dogs using an ensemble of ResNet models with PyTorch.

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