hamzafarooq/building-llm-applications-from-scratch resource

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2390 stars · 647 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Open-sourced course materials (code and slides) for building LLM-powered applications from scratch, covering transformer architecture, retrieval-augmented generation, fine-tuning, and deployment. It includes 29 lessons and 6 real-world projects taught to professionals at Stanford, UCLA, and other institutions.

Use cases

• learn to build llm applications from scratch
• understand transformer architecture in depth
• implement rag without langchain
• fine-tune and deploy open-source llms
• learn search and retrieval fundamentals for ai apps
• study course materials for building llm-powered systems

When to choose

• you want a structured, instructor-led curriculum on LLM application development
• you want to understand retrieval systems and RAG at a foundational level rather than using pre-built frameworks
• you have Python and basic ML knowledge and want hands-on projects

When to avoid

• you are a complete beginner without Python or ML background
• you want a production-ready library or framework rather than educational material
• you need a maintained software dependency - this is course content, not a tool

Facets

learning-resource · maturity active

machine-learning rag llm-training nlp search-engine large-language-models machine-learning tutorials education python course-materials jupyter-notebooks transformer-architecture fine-tuning open-source-llms slides retrieval-augmented-generation natural-language-processing

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