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