# patchy631/ai-engineering-hub

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

Repository: https://github.com/patchy631/ai-engineering-hub
Canonical: https://ross.abutalabs.com/products/ai-engineering-hub
Homepage: https://join.dailydoseofds.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: agents, ai, llms, machine-learning, mcp, rag
Last push: 2026-08-26T21:04:16+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 48
- inputs: {"age_days": 681, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 37186, forks 6144 (observed 2026-08-28T04:12:01.352633+00:00)

## What it is
A curated collection of 90+ production-ready tutorial projects on LLMs, RAG, AI agents, and MCP, organized by difficulty from beginner to advanced. Each project is a hands-on Jupyter Notebook example that can be implemented, adapted, and scaled in real applications.

## Use cases
- learn how to build RAG applications
- find example AI agent projects to adapt
- get started with LLM engineering as a beginner
- build a local OCR app with vision models
- learn MCP with real-world projects
- study fine-tuning and production AI systems
- follow an AI engineering learning roadmap

## When to choose
- you want hands-on, runnable notebook examples for LLM, RAG, and agent patterns
- you need projects sorted by skill level from beginner to advanced
- you want MIT-licensed code you can freely adapt into your own apps

## When to avoid
- you need a production-grade library or framework rather than tutorials
- you want a single maintained tool with API stability guarantees
- you need non-Python or non-LLM AI engineering resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: rag, agent-framework, llm-inference, mcp, ocr, chatbot, prompt-engineering
- domain: large-language-models, artificial-intelligence, tutorials, machine-learning
- platform: python, cross-platform
- tags: jupyter-notebooks, tutorials, example-projects, llm-apps, hands-on-learning, streamlit, ai-agents, retrieval-augmented-generation, natural-language-processing

## Member repositories
- patchy631/ai-engineering-hub (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:01.352633+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 2026-08-29T16:25:39.098312+00:00, confidence not recorded.
  - readme: https://github.com/patchy631/ai-engineering-hub (fetched 2026-08-28T04:12:01.352633+00:00, sha adc3cc301242)
  - homepage: https://join.dailydoseofds.com (fetched 2026-08-29T07:47:17.843117+00:00, sha 146606528d3f)
- Data as of 2026-08-30T08:39:29.467469+00:00.