Prompt Engineering using LangChain 🦜

Welcome to the Prompt Engineering using LangChain course! This is an ongoing hands-on tutorial series where we delve deep into mastering prompt engineering with LangChain, a powerful framework designed for building applications using large language models (LLMs).

Course Overview

This course is designed to guide you through various aspects of prompt engineering, focusing on LangChain's features and how they integrate with machine learning models. Each chapter will provide detailed hands-on learning through Jupyter Notebooks, and these are linked to the video tutorials in the course playlist.

Watch the full course playlist on YouTube:
Prompt Engineering using LangChain (YouTube)

Chapters Overview

🔹 Chapter 1: Foundations of Prompt Engineering

This chapter covers the basics of prompt engineering, including machine learning concepts, tokenizers, and LangChain’s role in NLP. It's great for beginners who want to understand how prompts work and how LangChain fits into the larger picture of LLMs.

🔹 Chapter 2: LangChain Integrations with External Models

Learn how to integrate LangChain with popular models such as OpenAI’s ChatGPT and HuggingFace models, and manage token usage with dotenv for secure key management. This chapter shows you how to link LangChain to external sources and systems effectively.

🔹 Chapter 3: Mastering Prompt Templates

Dive into dynamic and reusable prompts with LangChain's PromptTemplate. You will learn how to build customizable prompts that fit multiple use cases and make your prompts more adaptable and efficient.

🔹 Chapter 4: Effective Prompt Generation Techniques

In this chapter, we explore strategies for designing smart prompts that can help guide chatbots or language models to behave in the way you want. This is all about improving interactions between the model and the user.

🔹 Chapter 5: Few-shot Learning with LangChain

Learn how to apply few-shot learning with LangChain for tasks like Q&A, solving riddles, or even applying the technique to domain-specific applications. You’ll learn to train models with just a few examples.

🔹 Chapter 6: Output Parsing and Custom Responses

Explore techniques for extracting structured outputs from language models using LangChain’s built-in parsers and Pydantic-compatible formats. Perfect for those looking to handle the model's responses in a structured and reliable manner.

🔹 Chapter 7: Memory in LangChain

Memory plays a big role in prompt engineering. In this chapter, you’ll learn about different memory types in LangChain, such as:

• Conversation Buffer and Window Buffer
• Summary Memory and Entities Memory
• Memory Saving and Loading mechanisms This chapter covers how memory can be used to maintain state and ensure that the context is preserved across different interactions.

🔹 Chapter 8: QnA/RAG Techniques

Here, we dive into QnA (Question-Answering) and RAG (Retrieval-Augmented Generation) techniques. These methods combine the power of information retrieval and generation for improving answer quality. You'll learn about:

• Extractive vs Generative QnA
• Closed & Open QnA systems
• Working with NLP Vectors, and more.

🔹 Chapter 9: RAG Techniques with LangChain

In this chapter, we focus on Retrieval-Augmented Generation (RAG) techniques using LangChain. You’ll learn about:

• Document Chunking and Embedding processes
• How to Index documents and set up QnA Chains to improve model retrieval and response generation.

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