What you'll learn:
- Master LangChain to seamlessly integrate existing applications with potent Large Language Models (LLMs)
- Learn to connect to OpenAI’s language and embedding models
- Develop prompt engineering skills that improve performance and relevance of AI responses
- Apply the state-of-the-art Retrieval Augmented Generation (RAG) technique to empower your AI-driven product with a knowledge base
- Leverage AI to open up endless opportunities for your organization
- Enhance your career prospects with rare and highly sought-after AI Engineering skills
Are you an aspiring AI engineer excited to integrate AI into your product?
Are you thrilled about the breakthroughs in the field of AI?
Or maybe you’re eager to learn this new and exciting LangChain framework everyone’s talking about.
If yes, then you’ve come to the right place!
Why should you consider taking this LangChain course?
In this Build Chat Applications with OpenAI and LangChain course, we’ll explore the increasingly popular LangChain Python library to develop engaging chatbot applications.
With detailed, step-by-step guidance, you will use OpenAI’s API key to access their powerful large language models (LLMs). Once we have access to foundational models, we'll utilize LangChain and its integrations to create compelling prompts, add memory, input external data, and link it to third-party tools.
LangChain's integration with third-party tools distinguishes it by enabling connections to various language models and loading documents in multiple formats. It also allows for selecting suitable embedding models, storing embeddings in a vector store, and linking to search engines, code interpreters, and tools like Wikipedia, GitHub, Gmail, and more.
None of this would be possible without mastering the LangChain Expression Language (LCEL)—essential for developing stateful, context-aware reasoning chatbots. These chatbots remember past conversations, answer questions about unseen data, and tackle more complex problems.
Additionally, we’ll spend much of our time discussing the state-of-the-art Retrieval Augmented Generation (RAG), both theoretically and practically. This technique allows LLM-powered applications to analyze and answer questions about information outside their training data. Ultimately, we’ll create a chatbot that answers students’ questions on courses from the 365 library.
What skills do you gain?
- Integrate existing applications with powerful LLMs.
- Connect to OpenAI’s language and embedding models using an OpenAI API key.
- Develop prompt engineering techniques to enhance AI response performance and relevance.
- Implement RAG to enrich your AI-driven product with a knowledge base.
- Master the LCEL protocol—essential for developing applications with the LangChain Python library.
- Connect external tools to your LLM-powered application.
- Understand the mechanics behind agents and agent executors.
Enhance your career prospects with rare and highly sought-after AI Engineering skills by enrolling in this LangChain and OpenAI course.
Click ‘Buy Now’ and acquire real-world AI engineer skills today!
