# samwit/llm-tutorials

A set of LLM Tutorials from my youtube channel

Repository: https://github.com/samwit/llm-tutorials
Canonical: https://ross.abutalabs.com/products/llm-tutorials
Language: Jupyter Notebook
License Family: other
Last push: 2023-06-13T03:04:34+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1177, "days_push": 1177, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1163, forks 301 (observed 2026-08-28T04:03:49.657819+00:00)

## What it is
A collection of Jupyter Notebook tutorials accompanying Sam Witteveen's YouTube channel on large language models. It covers hands-on examples of working with LLMs, including prompting, agents, and related tooling.

## Use cases
- learn how to build LLM applications
- follow along with LLM youtube tutorials in notebooks
- examples of langchain and LLM agents
- getting started with prompt engineering
- study notebooks for RAG with LLMs

## When to choose
- you prefer learning by reading and running notebook code
- you want free tutorial material matching a YouTube course
- you are exploring LLM tooling like agents and prompting

## When to avoid
- you need production-ready, maintained library code
- you require a licensed or supported codebase
- you need up-to-date content for rapidly changed LLM APIs

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: llm-inference, prompt-engineering, rag, agent-framework
- domain: large-language-models, tutorials, artificial-intelligence, developer-tools
- platform: python, cross-platform
- tags: jupyter-notebooks, youtube-tutorials, langchain, openai, educational

## Member repositories
- samwit/llm-tutorials (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.657819+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:31:34.962904+00:00, confidence not recorded.
  - readme: https://github.com/samwit/llm-tutorials (fetched 2026-08-28T04:03:49.657819+00:00, sha 57abd955144d)
- Data as of 2026-08-30T08:39:29.467469+00:00.