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<h1>Generative AI Roadmap - 2026</h1>
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> **Learn AI/ML interactively at [AI-ML Companion](https://aimlcompanion.ai/)** - Guided walkthroughs, architecture decisions, hands-on challenges, and narrated overviews for every project.

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Your go-to hub for end-to-end GenAI learning. ⭐ Star this repo to stay updated with the latest GenAI resources :)


### 🔗 Quick Links
 [GenAI Essential Terms](https://github.com/genieincodebottle/generative-ai/blob/main/docs/essential-terms-genai.pdf) | [AI/ML/Agentic AI Scenario based Interview Q&A](https://aimlcompanion.ai/curriculum/interviewScenarios)| [GenAI Interview Q & A](https://github.com/genieincodebottle/generative-ai/blob/main/docs/genai-interview-questions.pdf) |  [Agentic AI Interview Q & A](./docs/agentic-ai-interview-questions.pdf) | [GenAI Usecases](./genai-usecases/) | [n8n Automation](./genai-usecases/n8n-automation/) |  [GenAI on Azure](https://github.com/genieincodebottle/generative-ai/blob/main/docs/genai-with-azure-cloud.pdf) | [GenAI on AWS](https://github.com/genieincodebottle/generative-ai/blob/main/docs/genai-with-aws-cloud.pdf) | [GenAI on VertexAI](https://github.com/genieincodebottle/generative-ai/blob/main/docs/genai-with-vertexai.pdf) |

### 📦 Comprehensive Prep Resources

Each phase is split by week and builds progressively.

> **Tip:** Expand or collapse each phase as you go using dropdowns (▸) to get all the relevant links.

<details>
<summary><strong>Phase 1: Foundation (Week 1-2)</strong></summary>

| Topics | Core Material| Additional Resources | Code |
|--------|------|-------|------|
| AI vs ML vs DL vs GenAI | [Video](https://www.youtube.com/watch?v=qYNweeDHiyU) | [GenAI Essential Terms](https://github.com/genieincodebottle/generative-ai/blob/main/docs/essential-terms-genai.pdf)||
| GenAI Intro | [GenAI in Nutshell](https://www.youtube.com/watch?v=2IK3DFHRFfw), [LLMs by 3b1b](https://youtu.be/LPZh9BOjkQs) | [LLM Blog](https://cohere.com/blog/llm-parameters-best-outputs-language-ai) ||
| Python Basics | [Crash Course](https://www.youtube.com/watch?v=kqtD5dpn9C8), [ML Crash](https://www.youtube.com/watch?v=7eh4d6sabA0) |[GitHub - Awesome Python](https://github.com/vinta/awesome-python)||
| ML & DL Basics | [ML 101](https://www.youtube.com/watch?v=i_LwzRVP7bg), [Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | [Crash DL](https://www.youtube.com/watch?v=VyWAvY2CF9c) ||
</details>

<details>
<summary><strong>Phase 2: NLP & Transformer Core (Week 3-4)</strong></summary>

| Topics                        | Core Material | Additional Resources | Code |
|------------------------------|---------------|-----------------------|------|
| NLP Basics                   | [YT - What is NLP?](https://www.youtube.com/watch?v=fLvJ8VdHLA0) |||
| Inside the Transformer       | [1. YT - Transformer Explained Visually by 3b1b](https://www.youtube.com/watch?v=wjZofJX0v4M) <br> [2. Blog - The Illustrated Transformer by Jay Alammar](https://jalammar.github.io/illustrated-transformer/) |||
| Tokenization & Embeddings    | [Blog - BPE Explainer (HuggingFace)](https://huggingface.co/learn/llm-course/en/chapter6/5) | [1. Blog - Embedding Explained](https://qdrant.tech/articles/what-are-embeddings/) <br> [2. Blog - Vector Db Explained](https://www.pinecone.io/learn/vector-database/) | [GitHub](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/embedding-models/embedding_models.ipynb) |

</details>

<details>
<summary><strong>Phase 3: LLM Fundamentals (Week 5-6)</strong></summary>

| Topics                     | Core Material | Additional Resources | Code |
|---------------------------|---------------|-----------------------|------|
| Multimodal LLMs Basics    | [YT - How do Multimodal AI models work?](https://www.youtube.com/watch?v=WkoytlA3MoQ) |||
| Mixture of Experts (MoE)  | [1. YT - What is Mixture of Experts?](https://www.youtube.com/watch?v=sYDlVVyJYn4) <br> [2. Blog - Visual Guide to MoE](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts) | [Blog - Huggingface MoE](https://huggingface.co/blog/moe) ||
| Open Weight LLMs (HF & Ollama) | [1. YT - Huggingface LLMs Locally](https://www.youtube.com/watch?v=Ay5K4tog5NQ) <br> [2. YT - Ollama](https://www.youtube.com/watch?v=h_GTxRFYETY) || [1. GitHub-HF](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/local_llms/huggingface/huggingface_models.ipynb) <br> [2. GitHub-Ollama](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/local_llms/ollama) |
| AI Frameworks             | [1. LangChain](https://python.langchain.com/docs/introduction/) <br> [2. LlamaIndex](https://docs.llamaindex.ai/en/stable/) |||
| Prompt Engineering        | [Coursera Specialization](https://www.coursera.org/specializations/prompt-engineering) | [Kaggle Whitepaper](https://www.kaggle.com/whitepaper-prompt-engineering) | [GitHub](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/prompt-engineering) |

</details>

<details>
<summary><strong>Phase 4: RAG & Agentic Systems (Week 7-8)</strong></summary>

| Topics                      | Core Material | Additional Resources | Code |
|----------------------------|---------------|-----------------------|------|
| Retrieval-Augmented Generation (RAG) | [1. Blog - Advance RAG Techniques](https://8738733.fs1.hubspotusercontent-na1.net/hubfs/8738733/eBooks/Weaviate-Advanced-RAG-Techniques-ebook.pdf) <br/> [2. PDF - RAG Decisions](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/advance-rag/docs/advance-rag-decision-flow-chart.pdf) || [1. GitHub - Advanced RAG (Graph RAG, Agentic RAG, Multimodal RAG etc)](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/advance-rag) <br/> [2. GitHub - Microsoft's Agentic RAG](https://github.com/microsoft/ai-agents-for-beginners/blob/main/05-agentic-rag/README.md) |
| Agentic AI                 | [1. Blog - Building Agents (Claude)](https://www.anthropic.com/research/building-effective-agents) <br> [2. Huggingface Agents Course](https://huggingface.co/learn/agents-course/en/unit0/introduction) | [1. Blog - Chip Huyen](https://huyenchip.com/2025/01/07/agents.html) <br/> [2. Google Whitepaper](https://www.kaggle.com/whitepaper-agents) | [1. GitHub-Agentic AI](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/agentic-ai) <br/>  [2. GitHub-NirDimant](https://github.com/NirDiamant/GenAI_Agents) <br/> [3. LangGraph](https://github.com/langchain-ai/langgraph/tree/main/examples/multi_agent) |

</details>

<details>
<summary><strong>Phase 5: Optimization & Evaluation (Week 9-10)</strong></summary>

| Topics                      | Core Material | Additional Resources | Code |
|----------------------------|---------------|-----------------------|------|
| Fine Tuning                | [YT - Shaw Talebi](https://www.youtube.com/watch?v=eC6Hd1hFvos) || [1. GitHub-ShawhinT](https://github.com/ShawhinT/YouTube-Blog/tree/main/LLMs/fine-tuning) <br> [2. GitHub-Unsloth](https://github.com/unslothai/unsloth?tab=readme-ov-file) |
| Inference Optimization & Quantization | [1. Course - Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth/) <br> [2. Blog - GGUF](https://huggingface.co/docs/hub/en/gguf) <br> [3. Blog - GGML](https://huggingface.co/blog/introduction-to-ggml) | [1. Unsloth](https://github.com/unslothai/unsloth) <br> [2. GPTQModel](https://github.com/ModelCloud/GPTQModel) ||
| Knowledge Distillation     | [Blog - Huggingface](https://huggingface.co/blog/Kseniase/kd) |||
| LLM Evaluation             | [1. Guide - LLM Evaluation](https://arize.com/llm-evaluation) <br> [2. Tool - RAGA](https://docs.ragas.io/en/stable/) <br> [3. Tool - Opik](https://www.comet.com/site/products/opik/) <br> [4. Tool - DeepEval](https://www.deepeval.com/) | [Blog](https://research.aimultiple.com/large-language-model-evaluation/) |  |
</details>

<details>
<summary><strong>Phase 6: Deployment & LLMOps (Week 11-12)</strong></summary>

| Topics                      | Core Material | Additional Resources | Code |
|----------------------------|---------------|-----------------------|------|
| LLMOps                     | [1. Coursera Specialization](https://www.coursera.org/specializations/large-language-model-operations) <br> [2. Deeplearning AI Course](https://www.deeplearning.ai/short-courses/llmops/) |||
| GenAI in Production        | [YT - RAG App on AWS](https://www.youtube.com/watch?v=x2P4Ee6PYNg) || [GitHub Code](https://github.com/genieincodebottle/rag-app-on-aws) |
| Safety & Alignment         | [1. LlamaGuard](https://cloudyuga.guru/blogs/securing-ai-applications-with-llamaguard/) <br> [2. Prompt Guard](https://www.llama.com/llama-protections/) | [Constitutional AI](https://arxiv.org/html/2501.09004v1) | Work in Progress |
| LLM Leaderboard            | [1. Chatbot Arena](https://lmarena.ai/?leaderboard) <br> [2. Artificial Analysis AI](https://artificialanalysis.ai/leaderboards/models) <br> [3. Aider](https://aider.chat/docs/leaderboards/) |||

</details>


### 🛠️ Tools | Frameworks

| Category  | Link  | 
|------------|----------------|
| IDE & Dev Playground  |[Colab NoteBook](https://colab.research.google.com/notebooks/basic_features_overview.ipynb), [Kaggle Notebook](https://www.kaggle.com/code), [VSCode](https://code.visualstudio.com/docs/introvideos/basics), [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview)||
| LLM Aggregators  |[litellm](https://github.com/BerriAI/litellm), [Openrouter](https://openrouter.ai/docs/quickstart), [Aisuite](https://github.com/andrewyng/aisuite)||
| Agentic AI Frameworks |[CrewAI](https://docs.crewai.com/introduction), [Relevance AI](https://relevanceai.com/), [LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/), [SuperAGI](https://superagi.com/), [SmolAgent](https://huggingface.co/blog/smolagents), [OpenAI Agent SDK](https://openai.github.io/openai-agents-python/), [Google's ADK](https://google.github.io/adk-docs/)||
| AI Automation (Workflow + AI Integration)  |[n8n](https://n8n.io/), [Zapier](https://zapier.com/), [Make](https://www.make.com/en)||
| Cloud Agentic Framework  |[Microsoft Agent](https://github.com/microsoft/Agents), [AWS Bedrock Agent](https://aws.amazon.com/bedrock/agents/)||
| Model Context Protocol (MCP) |[YT- MCP Clearly Explained](https://www.youtube.com/watch?v=7j_NE6Pjv-E) <br> [YT- MCP Crash Course for Python Developers](https://www.youtube.com/watch?v=5xqFjh56AwM) <br> [Official Claude Doc](https://modelcontextprotocol.io/introduction) <br> [GitHub-Awsome MCP Servers](https://github.com/punkpeye/awesome-mcp-servers)||
| Agent2Agent Protocol (A2A) |[Video - Demo](https://storage.googleapis.com/gweb-developer-goog-blog-assets/original_videos/A2A_demo_v4.mp4) <br> [Specification](https://github.com/google/A2A) <br> [GitHub](https://github.com/google/A2A)||
| AI Coding Assistants |[Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview), [Gemini CLI](https://blog.google/technology/developers/introducing-gemini-cli-open-source-ai-agent/), [Cursor](https://www.cursor.com/), [Bolt](https://bolt.new/), [Lovable](https://lovable.dev/), [Replit](https://replit.com/), [V0](https://v0.dev/) ||
| Learning Platform  |[AI-ML Companion](https://aimlcompanion.ai/) - Interactive AI/ML learning platform with 17 tracks, 250+ modules, visualizations, quizzes, and hands-on coding (ML fundamentals → LLMs → MLOps)||

### 💡 GenAI Use Cases

| Usecase  | Link  | 
|------------|----------------|
| Example Code for Running API-Based LLM | [Groq](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/groq.ipynb), [Claude](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/claude.ipynb), [OpenAI](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/openai.ipynb), [Gemini](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/gemini.ipynb), [DeepSeek](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/deepseek.ipynb), [Cohere](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/notebooks/cohere.ipynb) |
| Example Code for Running LLMs Locally | [HuggingFace](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/llm-providers/local_llms/huggingface/huggingface_models.ipynb), [Ollama Local](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/llm-providers/local_llms/ollama), [Ollama at Colab](https://colab.research.google.com/drive/1TrB6dLCSBdSDXcauQ1lofgWDiiEbQXHw?usp=sharing), [LMStudio](https://lmstudio.ai/) |
| Run FastMCP based Websearch Remote MCP Server Locally |[GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/mcp)|
| Production-Grade GenAI on AWS: RAG with Remote MCP Server |[YouTube](https://www.youtube.com/watch?v=x2P4Ee6PYNg) <br/> [GitHub](https://github.com/genieincodebottle/rag-app-on-aws)|
| Llama 4 Scout – All-in-One App for Chat, OCR, RAG & Agentic AI with CrewAI |[GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/llama-4-multi-function-app)|
| Advanced Snapshot-Based PDF Parsing using Docling, Markitdown, Gemini, Llama 4, Claude, GPT-4 & more |[YouTube](https://www.youtube.com/watch?v=26thuRsxiUc) <br> [GitHub](https://github.com/genieincodebottle/parsemypdf)|
| Optical Character Recognition Using Advanced Vision-Language Models |[GitHub](https://github.com/genieincodebottle/parsemypdf/tree/main/vlm_ocr)|
| Chat with Neo4j Graph DB  |[YouTube](https://www.youtube.com/watch?v=PJTxPW5He7w) <br> [GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/graph-qa)|
| LLM-Based Chatbot for SQL Data Interaction |[GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/archive_legacy/text-to-sql)|
|  LLM-Driven Chatbot to Interact with CSV Data |[YouTube](https://www.youtube.com/watch?v=c7mwwfsBGZ8) <br> [GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/archive_legacy/csv-rag)|
| Cache-Augmented Generation (CAG) |[GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/cache_augmented_generation) |
| AI Chat Analyzer for ChatGPT & Claud |[GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/your_ai_chat_analytics) |

### 🔖 Key Downloads

| Category | Links |
|-------------|---------|
| Prompt Engineering       | [GitHub](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/prompt-engineering) |
| RAG   | [Pdf](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/advance-rag/docs/advance-rag-decision-flow-chart.pdf) |
| Agentic AI   | [GitHub](https://github.com/genieincodebottle/generative-ai/tree/main/genai-usecases/agentic-ai) |
| Misc   | [Embedding Models](https://github.com/genieincodebottle/generative-ai/blob/main/genai-usecases/embedding-models/embedding_models.ipynb), [GenAI Project Lifecycle](https://github.com/genieincodebottle/generative-ai/blob/main/docs/genai-project-lifecycle.pdf) |


### 📄 Imp Research Papers

| Title | Links |
|-------------|---------|
|Kimi K2: Open Agentic Intelligence|[Link](https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf)|
|Gemini 2.5|[Link](https://storage.googleapis.com/deepmind-media/gemini/gemini_v2_5_report.pdf)|
|DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning|[Link](https://arxiv.org/abs/2501.12948)|
|DeepSeek-V3 Technical Report|[Link](https://arxiv.org/abs/2412.19437)|
|Mixtral of Experts|[Link](https://arxiv.org/abs/2401.04088)|
