# genieincodebottle/generative-ai

Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.

Repository: https://github.com/genieincodebottle/generative-ai
Canonical: https://ross.abutalabs.com/products/genieincodebottle-generative-ai
Homepage: https://aimlcompanion.ai/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: generative-ai, agentic-ai, claude, gemini, genai, genai-usecase, interview-questions, langchain, langgraph, large-language-model, llm-agent, llm-evaluation, mcp, model-context-protocol, multimodal, n8n, n8n-workflow, openai-api, retrieval-augmented-generation, agentic-framework
Last push: 2026-08-24T06:24:56+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 69
- inputs: {"age_days": 967, "days_push": 9, "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 2608, forks 631 (observed 2026-08-28T04:07:04.176898+00:00)

## What it is
A curated collection of Generative AI learning resources including a detailed roadmap, hands-on Jupyter Notebook projects, use cases, and interview/coding preparation material. It accompanies the AI-ML Companion interactive learning platform covering foundations through agentic AI and MLOps.

## Use cases
- learn generative ai from scratch with a roadmap
- prepare for genai interview questions
- find hands-on llm and langchain projects
- understand retrieval augmented generation with examples
- study agentic ai and multi-agent systems
- learn model context protocol and llm evaluation
- practice genai coding interview problems

## When to choose
- you want a structured, continuously updated GenAI learning path
- you prefer notebook-based hands-on projects alongside theory
- you are preparing for AI/ML or GenAI job interviews

## When to avoid
- you need production-ready software or a deployable library
- you want a single focused tool rather than broad educational material

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, rag, agent-framework, prompt-engineering, mcp, data-science
- domain: artificial-intelligence, machine-learning, large-language-models, tutorials, education
- platform: python, cross-platform
- tags: generative-ai, roadmap, interview-preparation, jupyter-notebooks, langchain, langgraph, n8n, agentic-ai, llm-evaluation, multimodal, ai-agents, retrieval-augmented-generation

## Member repositories
- genieincodebottle/generative-ai (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.176898+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 2026-08-30T02:20:55.345680+00:00, confidence not recorded.
  - readme: https://github.com/genieincodebottle/generative-ai (fetched 2026-08-28T04:07:04.176898+00:00, sha a125237be00d)
  - homepage: https://aimlcompanion.ai/ (fetched 2026-08-29T10:03:46.039604+00:00, sha feea592a0ce9)
- Data as of 2026-08-30T08:39:29.467469+00:00.