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Course level: Intermediate
Learn how to improve the accuracy and reliability of LLM-based apps by implementing Retrieval-augmented Generation (RAG) using embeddings and a vector database.
This course contains 19 interactive scrims
What is an embedding?
Setting up a vector database
Supabase & pgvector
Semantic search
Similarity search
Chunking text documents
RAG
Find the perfect movie for movie night every time using Embeddings.
Before taking this course, you should have an intermediate level understanding of Vanilla JS including working with APIs and async JavaScript. Below are our suggested resources to get you up to speed.
Lifelong learner, enthusiastic about changing lives through tech. Enjoys water sports and exploring the South Florida waters. 🏄🏻♂️ ☀️
This course will teach you how to craft and use embeddings in vector databases.
Start off by getting the hang of embeddings and why they're key in AI's thinking process.
Then you'll get hands-on practice, as you'll be chunking text documents, generating embeddings, and plugging them into vector databases using tools like Supabase.
As you build out your app, you will use similarity searches to find the relevant embeddings in your vector database.
Finally, you'll combine these results with the ChatCompletions API from OpenAI to create human-like chat responses.
This course is a mix of theory and interactive challenges. By the end, you won't just get the tech stuff; you'll actually have built a proof-of-concept AI Movie Recommendation engine that you can add to your portfolio.
to the QAI (artificial intelligence) is like having a super-smart computer buddy who can do things that humans normally do, like recognize pictures of cats or tell you what the weather is like outside. They're kind of like a cross between R2-D2 and Hermione Granger - nerdy, helpful, and always up for a challenge! Just don't expect them to have feelings or opinions on whether pineapple belongs on pizza.
The OpenAI API gives us access to AI models in our apps. By interacting with the API, we can leverage the power of these AI models to perform a wide range of tasks, such as natural language understanding, text generation, image generation, and more.
To “embed” means to place one object into a different space. In AI, we often create embeddings of text snippets, transforming the characters into an array of numbers. This enables the AI to better understand the underlying meaning of the text snippet.
