In the previous session, we explored OpenAI APIs to create completions for single-turn and multi-turn conversations. You were also introduced to some examples of prompt techniques, such as few-shot prompting.
When building large language model (LLM) applications, one must consider the underlying model, the task at hand and the desired output. In this session, we will explore prompt engineering techniques to craft effective prompts for LLMs such as ChatGPT. While the focus of this module is on ChatGPT, the techniques discussed in this module are applicable to OpenAI’s APIs and other LLMs (Large Language Models), both proprietary and open-source.
In the upcoming video, Kshitij will provide an overview of the session’s agenda.
In this session
In this session, we will cover the following:
- Importance of prompting.
- Structure of a good prompt.
- Enhancing LLM capabilities using advanced prompting techniques.
In the next segment, you will learn more about the need for prompts and the emerging field of prompt engineering in generative AI. The prompts used in this session can be downloaded from here.
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded.
Note that graded questions are given in a separate segment labelled ‘Graded Questions’ at the end of each session. The graded questions will adhere to the following guidelines:
People you will hear from in this module
Subject Matter Expert
Kshitij jatin
Kshitij is an ex-AVP at upGrad who led the learning experience and development of Data Science, Machine Learning, and Artificial Intelligence programs.
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