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.

Featured book

• Post-Training Mastery
  RLHF, DPO, reward modeling, and alignment — read in-browser or download the PDF (~600 pages, Mohammed Saqlain).

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• Fixes: typos, broken links, outdated facts, and clearer explanations.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.