In this episode, we dive into the futuristic concepts of Zero Shot and Few Shot Learning in large language models. We explore how these models can perform tasks without specific training through emergent reasoning, task inference, and knowledge synthesis. The episode explains the stages of zero shot and few shot prompting, compares their computational costs, and provides practical tips for writing effective prompts. We also discuss the trade-offs between both techniques and emphasize the importance of clarity, specificity, and structure in prompting to harness the full potential of AI.00:00 Introduction to Futuristic Learning Models00:38 Understanding Zero Shot Learning01:26 How Zero Shot Prompting Works03:27 Diving into Few Shot Learning05:50 Trade-offs Between Zero Shot and Few Shot09:00 Practical Tips for Writing Effective Prompts11:20 Conclusion and Future of AI

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