š Getting consistent structured JSON output from GPT is important for feeding it into downstream processes.
š Using a Google Colab notebook, we can install the necessary packages and obtain the desired JSON output from GPT.
š The example discussed in the video focuses on obtaining a children's story as JSON format with metadata.
š The video explains how to get structured JSON output from the OpenAI GPT API consistently.
š¢ The method involves using prompt engineering to specify the desired output format and content.
š» The API call to the GPT model returns a structured JSON response with a title, story text, top five difficult words with definitions, and word count.
š” Json format allows us to easily access and manipulate data using keys.
š Function calling is an alternative method to specify complex data structures in OpenAI GPT API.
š The function calling method requires defining functions and parameters to deliver specific outputs.
š” Making API calls with modified messages to extract relevant data.
š Extracting function called arguments as the desired output.
š Missing word count and top five words in the response.
š Few-shot learning can help improve the output of the OpenAI GPT API by providing additional information.
š§© Creating an example with a specific theme, like dinosaurs, can guide the API to generate the desired output.
š” Passing the example object, including key-value pairs, to the API helps in receiving a well-structured JSON output.
š The video explains how to obtain structured JSON output consistently from the OpenAI GPT API.
š By providing example requests and responses, the AI model can generate more accurate and improved outputs.
š The video demonstrates adding complex nested structures, such as lists inside objects, to the JSON output schema.
š Using feature calling consistently retrieves nested structured Json content.
š§© The desired format includes the title, story text, word count, and top five words.
ā There may be instances where the word count is missing in the output.
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