Build An AI Assistant Using SEC Filings, LangChain & FAISS

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Description

Learn to build a fully functional Retrieval-Augmented Generation (RAG) assistant from scratch, the same architecture behind modern AI tools that answer questions from real documents instead of guessing.

In this hands-on course, you'll use a real SEC financial filing (Apple's 10-K report) as your working dataset. You'll learn how to pull the document, parse it, split it into searchable chunks, generate embeddings, build a FAISS vector index, and connect the whole pipeline to a GPT-powered assistant that answers questions grounded in the actual filing, not hallucinated content.

By the end, you'll understand exactly how RAG assistants work under the hood and walk away with a working project you can show a client or an employer.

What you'll learn:

What RAG is and why businesses use it over a standard AI assistant
How to source and clean real-world documents for an assistant's knowledge base
Text chunking and embedding generation with OpenAI
Building and querying a FAISS vector store
Connecting retrieval to a language model for grounded, accurate answers
Common pitfalls in RAG systems and how to debug them

Who this is for: Beginners to intermediate learners. Basic Python knowledge (variables, functions, loops) is enough to follow along; no prior AI or machine learning background required.

Format: Written, step-by-step modules with tested code and an assessment question per module. Video walkthroughs coming in a follow-up release.

Delivered by Jaybeetech Academy (compay.pro).

Requirements

After purchase, you'll receive all 8 course files (a course overview plus one file per module) along with the course banner image.
Start with the overview file, then work through the modules in order, Module 1 through Module 7. Each module builds on the code from the one before it.
Open Google Colab (colab.research.google.com) and create a new notebook to follow along, writing and running the code as you go through each module rather than just reading it.
Before Module 2, create a free OpenAI account and generate an API key. You'll need this to run the embedding and chat completion steps from Module 2 onward.
Complete the assessment question at the end of each module before moving to the next one. These confirm you understood the concept, not just copied the code.
Finish with the Module 7 capstone project, applying the full pipeline to a new document of your choice.
For any code that doesn't run as expected, check the "Practical Notes" section in that module first, it covers the most common errors and how to fix them.

What You'll Learn

What RAG is and why businesses use it over a standard AI assistant How to source and clean real-world documents for an assistant's knowledge base Text chunking and embedding generation with OpenAI Building and querying a FAISS vector store Connecting retrieval to a language model for grounded

accurate answers Common pitfalls in RAG systems and how to debug them

Course Content

0h 15min

Course overview section