This video is part of a full Udemy course which will be uploaded soon on Udemy.
We explore how few-shot prompting allows you to provide a few examples to guide the API in generating accurate and relevant responses. Perfect for complex tasks where context and specificity matter.
In This Video:
🔍 Introduction to Few Shot Prompting: Learn the fundamentals of few-shot prompting and its benefits over zero-shot and one-shot prompting.
💡 Creating Effective Few Shot Prompts: Strategies for designing prompts with examples to achieve optimal results.
🚀 Understanding Limitations: Few-shot prompting is limited to internal knowledge and producing a thought chain in giving answers.
Timestamps:
00:00 - Introduction to the Video
00:30 - Understanding Few Shot Prompting
01:50 - Coding The API
04:14 - Limitations of Few-Shot Prompting
06:18 - 2nd Example to Explain Limitations
Other courses:
RAG LLMOps in GCP - Deploying a Retrieval Augmented Generation LLM in GCP infrastructure project: https://youtu.be/39PGfKA50As
Source code for these tutorials: https://github.com/Sahilvohra58/open_ai_api_tutorials
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Thank you for watching, and happy coding! 🚀
