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.

Contribution guidelines

Contributions are welcome. This repo is static HTML and CSS only, with no build step.

What you can contribute

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• Fixes: typos, outdated facts, broken links, and clearer explanations.

How to contribute

1. Fork Engineering-Knowledge-Base on GitHub.
2. Create a branch with a clear name (e.g. add-rag-interview-questions).
3. Make your changes and open a pull request against main.
4. In the PR description, explain what changed and why.

Conventions

• Keep shared files at the repo root: index.html, styles.css, and assets/.
• Put each subject in its own folder (e.g. AI Engineer/).
• Match existing pages: breadcrumb nav, learn-page on learn articles, footer links to the subject hub.
• Use relative paths for links and styles (e.g. ../../styles.css from learn pages).
• For diagrams, use small refer-link citations to external sources.

Questions or ideas? Open an issue on GitHub.