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An AI-powered learning assistant prototype for navigating technical documentation using RAG (Retrieval-Augmented Generation), built and documented over a 10-week learning sprint.

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Embedded Intelligence: An AI-Powered Learning Assistant for Technical Documentation

This repository contains the development of an AI-powered knowledge assistant that helps users navigate technical documentation using Retrieval-Augmented Generation (RAG). The project is being built over a structured 10-week plan and explores technologies like LLMs, vector databases, LangChain, and Streamlit — with a focus on applications in technical enablement, onboarding, and knowledge management.


📌 Project Goal

To build a hands-on RAG-based prototype that:

  • Retrieves relevant knowledge from technical documentation
  • Generates contextual, verifiable responses using LLMs
  • Adds learning-focused features like guided flows, concept linking, and embedded knowledge hygiene tips
  • Demonstrates how modern AI tools can support documentation-driven learning at scale

🧱 Tech Stack (WIP)

  • Language: Python
  • LLMs: Azure OpenAI / OpenAI GPT-4
  • Vector DB: ChromaDB (local), Pinecone (cloud), or Azure Cognitive Search
  • Frameworks: LangChain / LlamaIndex
  • Interface: Streamlit or Gradio
  • Other: Docker, GitHub Actions, RAGAS (evaluation)

🗓️ Timeline

This project is being developed across a 10-week learning sprint, with weekly milestones and blog posts published along the way.

Week Focus Area
0 Foundations, Intentions & Direction
1 Project Scope, Setup, Data & Market Research
2 Document Parsing & Embeddings
3 Vector Store Setup & Retrieval
4 LLM Integration & Basic RAG Q&A
5 Refining Retrieval + Prompting
6 Evaluation Framework
7 Learning-Focused Features
8 UI Build (Streamlit/Gradio)
9 Dockerization & Deployment
10 Final Polish & Strategy Write-Up

🔍 Use Case

This assistant is designed to help technical and non-technical employees — especially new hires — quickly locate relevant information across sprawling internal documentation repositories (e.g., Confluence, SharePoint, GitHub wikis), while offering embedded micro-learning and support.


✍️ Blog Series

Weekly blog posts will be published on JenShannon.tech and linked here once available:


⚠️ Disclaimer: Learning in Public

This project is a learning experiment developed in public. I’m building as I learn — expect rough edges, questions-in-progress, and plenty of iteration. Contributions and feedback are welcome (especially if you're also exploring AI + knowledge systems)!


📝 License

This project is licensed under the MIT License. Feel free to fork, build on, or contribute — attribution appreciated.


🙋‍♀️ About Me

Hi, I’m Jen Shannon, a technical learning and enablement leader passionate about AI, knowledge management, and workforce development. I'm using this project to explore how we might bring learning and documentation closer together in modern organizations.

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An AI-powered learning assistant prototype for navigating technical documentation using RAG (Retrieval-Augmented Generation), built and documented over a 10-week learning sprint.

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