How does Retrieval-Augmented Generation (RAG) improve the accuracy of a JavaScript AI application?

RAG retrieves relevant information from a knowledge source and supplies it to the model, helping produce more grounded, context-specific answers.

What can LangChain.js be used for in generative AI development?

LangChain.js helps JavaScript developers build AI workflows that connect LLMs with prompts, tools, memory, data sources, and retrieval pipelines.

How can I run an AI model locally with Ollama instead of relying on cloud inference?

Install Ollama, download a supported model, and access it through its local API or command line from your JavaScript application.

Free online course Generative AI

Duration of the online course: 1 hours and 43 minutes

4.3

(3)

Build practical Generative AI skills with a free online course: prompts, RAG, LangChain.js, local models, and Azure tools to ship real apps faster.

In this free course, learn about

How to build generative AI apps using JavaScript end-to-end
Why JavaScript is well-suited for AI apps (ecosystem, web/server reach, tooling)
Core LLM concepts and key capabilities (generation, reasoning patterns, context use)
Essential prompt engineering techniques for better, more controllable outputs
Methods to improve response accuracy (e.g., grounding, constraints, eval/iteration)
How Retrieval-Augmented Generation (RAG) boosts reliability by using external data
Using LangChain.js to orchestrate prompts, tools, retrieval, and app workflows
Running models locally with Ollama and benefits (privacy, offline dev, cost control)
Getting started with free/small models like Phi-3 for prototyping and learning
Using browser-based tools to try models without needing powerful GPU/CPU hardware
Basics of Azure AI Studio and the first steps to create/test generative AI projects
Building AI apps with Azure Cosmos DB and its advantages (scale, low-latency, vectors)
Designing “chat with your data” apps via chunking and vectorizing data for retrieval
Streaming LLM output with the AI Chat Protocol and why inference services help streaming

About the free online course

Generative AI is reshaping how products are built, how teams automate work, and how developers deliver experiences that feel truly conversational. This free online course helps you move from curiosity to confident, hands-on capability by showing how modern AI apps are designed, tested, and improved using a JavaScript-friendly workflow. If you want to prototype quickly, integrate models into real software, and understand what makes results accurate and reliable, this course is a practical starting point.

You will learn how large language models work at a high level and why they are powerful in everyday applications such as assistants, content generation, summarization, classification, and coding support. You will also develop strong prompt engineering habits so you can guide a model toward clearer, more consistent outputs and reduce ambiguity. As you progress, the focus shifts from simple prompts to building more dependable systems, where evaluating answers and improving response quality becomes part of the development process rather than an afterthought.

A key theme is grounding AI in trustworthy information. You will see how Retrieval-Augmented Generation can help a model respond with context drawn from your own sources, which is essential when you need answers tied to internal docs, knowledge bases, or domain-specific data. From there, you will explore how developer tooling can accelerate your builds, including ways to orchestrate calls, manage prompts, and connect components using JavaScript-first libraries. You will also discover options for running models locally for privacy, experimentation, and faster iteration, as well as lightweight ways to try models without expensive hardware.

Finally, the course connects these techniques to cloud-based workflows by introducing modern platforms and data stores that support AI application development. You will gain a clearer view of how to structure an end-to-end generative AI solution, how data can be prepared for vector search and chat over your content, and why streaming responses improves user experience in real interfaces. By the end, you will be equipped to plan, prototype, and refine generative AI features that are grounded, responsive, and ready to integrate into real products.

Course content

Video class: Generative AI with JavaScript - Introduction 02m
Exercise: Why is JavaScript considered a good choice for creating AI applications?
Video class: What you need to know about LLMs [Pt 1] 07m
Exercise: What is a key capability of large language models (LLMs)?
Video class: Essential prompt engineering techniques [Pt 2] 10m
Exercise: What is a technique to improve accuracy in model responses?

Video class: Improve AI accuracy and reliability with RAG [Pt 3] 05m
Exercise: What is the main advantage of using Retrieval-Augmented Generation (RAG) with AI models?
Video class: Speed up your AI development with LangChain.js [Pt 4] 12m
Exercise: What is Longchain JS primarily used for?
Video class: Run AI models on your local machine with Ollama [Pt 5] 07m
Exercise: What is a key benefit of using Oama for local AI model deployment?

Video class: Get started with AI for free using Phi-3 [Pt 6] 04m
Exercise: What tool allows you to experiment with AI models directly from a browser without needing a powerful GPU or CPU?
Video class: Introduction to Azure AI Studio [Pt 7] 10m
Exercise: What is the first step in working with generative AI applications in Azure AI Studio?
Video class: Building Generative AI Apps with Azure Cosmos DB [Pt 8] 12m
Exercise: What is one of the key advantages of using Azure Cosmos DB for AI applications?

Video class: Azure tools 22m
Exercise: What type of AI application allows you to speak to your own data by identifying and vectorizing specific data parts?
Video class: Streaming Generative AI output with the AI Chat Protocol [Pt 10] 07m
Exercise: What is the main advantage of using an AI inference service over browser-side inference for streaming generative AI output?

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