Engineering Knowledge Base

Subjects

Pick a subject to explore learn notes and interview prep.

• AI Engineering
  LLMs, pretraining, fine-tuning, RAG, vector databases, inference, deployment, and agents.
• Docker & Kubernetes
  Containers, Docker Compose, image security, Kubernetes architecture, workloads, networking, storage, RBAC, Helm, and observability.
• Graph Machine Learning
  Graphs, message passing, GCN, GraphSAGE, GAT, graph transformers, training, data prep, GNNs with LLMs, and recent research.

Open Interactive Labs

Contributing

This project is static HTML and CSS with optional client-side search. Contributions are welcome via GitHub pull request.

Full documentation: README.md

What to contribute

• New subjects: subject folder, hub page, and learn/ / interview/ tracks.
• Learn pages: concepts, tradeoffs, and short code examples where useful.
• Interview Q&A: expandable <details> questions paired with learn topics.
• Fixes: typos, broken links, outdated facts, and clearer explanations.

Workflow

1. Fork Engineering-Knowledge-Base on GitHub.
2. Create a branch (for example add-rag-interview-questions).
3. Make changes and open a pull request against main.
4. Describe what changed and why in the PR.

Conventions

• Shared root files: index.html, styles.css, assets/, scripts/.
• One folder per subject (for example AI Engineer/).
• Match existing layout: breadcrumbs, learn-page on learn articles, footer hub links.
• After adding or renaming pages, run powershell -File scripts/build-search-index.ps1 and commit the updated index files.
• Use relative paths for styles and links (see README for depth table).
• Use refer-link for external diagram and paper citations.

Questions or bugs: open a GitHub issue.