Zero, One, and Few Shot Prompting with Langchain and OpenAI LLMs

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In this python coding video we delve into the realms of zero-shot, one-shot, and few-shot prompting using the powerful combination of Langchain and OpenAI with large language models

Zero-shot, one-shot, and few-shot prompting explained
Practical applications and use cases
Demonstration of Langchain and OpenAI in action

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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.

This YouTube channel serves as both a platform for sharing knowledge and a personal journey of continuous learning. With a commitment to growth, I aim to expand my skill set by publishing 2 to 3 new videos each week, delving into various aspects of data analytics/science and Artificial Intelligence. Join me on this exciting journey as we explore the endless possibilities of data together.

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This is great. Got a doubt, does few shot send the full details to llm everytime and will it increase the tokens on every call

sudharsanbabus
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Thanks Ryan! Keep up with this series. Could you provide a public notebook with the code used in the video?

daviderizzello