/explore
|
You are here 
|
blog.mozilla.ai | ||
| | | | |
isthisit.nz
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. |
|
| | | | | ||
| | | | |
callmephilip.com
Notes for Jeremy Howard's video A Hackers' Guide to Language Models |
|
| | | | | ||
| | | | |
til.simonwillison.net
Here's the pattern I figured out for using the openai Python library to extract structured data from text using a single call to the model. |
|
| | | | | ||
| | | | |
blog.daniemon.com
How to use ChatGPT function calling to have better control of the API's response, making it easier to use the generated content in your code. |
|
| | | |||
