
Create a Google Gemini API key in AI Studio and integrate it into your Lang Chain workflow to run models alongside or instead of OpenAI.
Set up a secure OpenAI API key with dotenv in a JavaScript project, load it via LangChain, run a GPT-3.5 instruct LLM, and display the response.
Explore how LangChain enables building generative AI apps by managing prompts and chat history, outputting JSON, XML, or CSV, and assembling multi-step chains with data sources like Wikipedia and SQL.
Learn to build a legacy llm chain with LangChain's lm chain, using prompt templates and an output parser to generate course-specific, personalized responses.
Explore how language models generate output token by token and tune their behavior with temperature, top-p, and top-k to balance determinism and creativity.
Experiment with llm parameters such as temperature, top p, top k, and max tokens to control determinism, creativity, and cost in javascript demos.
Explore building and testing a backend rag architecture with LangChain for JavaScript developers, including retrieval and generation chains, a terminal chat utility, and streaming vs. instant responses.
Explore using git history to build a conversational lag solution that answers follow-up questions from chat history. Contextualize queries with chat history and vector embeddings to improve rag retrieval.
** Nov 2025 update: This course is updated with the latest langchain version v1.0. Google Gemini free tier support added to the course code, you can now do this course with both OpenAI and Google Gemini with additional lectures on AWS and Anthropic**
Welcome to the Generative AI and LangChain Course for JavaScript Developers! This course is tailored specifically for JavaScript professionals ready to advance their careers in the rapidly growing field of generative AI. While AI and machine learning have traditionally been dominated by Python, generative AI has opened up new possibilities, allowing JavaScript developers to build high-quality, LLM powered applications.
Who Should Take This Course? This course is designed for developers and architects with JavaScript and Node.js experience who are eager to build applications powered by large language models (LLMs). You’ll learn how to use JavaScript with LangChain to create generative AI applications, mastering core concepts like RAG (retrieval-augmented generation), embeddings, vector databases, and more. By the end, you’ll be equipped to develop robust generative AI applications.
Course Journey: We start with setting up the development environment, creating basic applications to explore key frameworks. Then, we’ll dive into advanced topics, building real-world applications with features like retrievable augmented generation and adding conversational layers with chat history.
Key Topics Covered:
LangChain with JavaScript/TypeScript
LLMs: Working with top providers like AWS Bedrock, GPT, and Anthropic
Prompts & PromptTemplates
Output Parsers
Chains: Including legacy chains and LCEL
LLM Parameters: Temp, Top-p, Top-k
LangSmith
Embeddings & VectorStores (e.g., Pinecone)
RAG (Retrieval Augmentation Generation)
Tools: Web crawlers, document loaders, text splitters
Memory & Chat History
Throughout the course, you’ll engage in hands-on exercises and build real-world projects to reinforce each concept, ensuring a solid foundation in generative AI with JavaScript. By course completion, you’ll be proficient in using LangChain to develop versatile, high-performance LLM applications.
What’s Included? This course is also a community experience. With lifetime access, you’ll receive:
GitHub repositories with complete course code
Access to an exclusive Discord community for support and discussion on GenAI topics
Free updates and continuous improvements at no extra cost
Disclaimers:
This is not a beginner course; software engineering experience and some experience in JavaScript are assumed.
We will be using the VSCode IDE (though any editor is welcome).
Some LLM services may require payment, but we’ll utilize free options whenever possible.
The views and opinions expressed here are my own and do not represent those of my employer.