📚 Learning Paths in Gen AI

 Here’s a practical guide to 📚 Learning Paths in Generative AI (Gen AI) — whether you're a beginner, developer, data scientist, or product leader. Generative AI spans multiple disciplines like machine learning, NLP, deep learning, and prompt engineering, so it’s important to structure your learning based on your role and goals.


🎯 1. Choose Your Track (Based on Role)

Role Focus Areas

Beginner/Explorer Concepts, tools, hands-on with ChatGPT/Midjourney/etc.

Developer APIs (OpenAI, Anthropic, Hugging Face), prompt design, app integration

Data Scientist Model training, fine-tuning, evaluation, embeddings

Product/Business Use cases, responsible AI, adoption strategies

Creative Professional Text-to-image/video, story generation, creative prompting


🧭 2. Beginner Path: Foundational Concepts

🟢 Goal: Understand what Gen AI is and how to use it.


Topics:

What is Generative AI?


LLMs (e.g. GPT, Claude, Gemini)


Prompt engineering basics


Use cases (text, image, code, audio)


Tools: ChatGPT, DALL·E, Midjourney, GitHub Copilot


Recommended Resources:

Elements of AI


OpenAI’s Intro to Prompt Engineering


YouTube: Lex Fridman, Yannic Kilcher, Two Minute Papers


👨‍💻 3. Developer Path: Build with Gen AI

🟠 Goal: Learn to integrate and build AI-powered apps.


Topics:

OpenAI / Claude API usage


LangChain / LlamaIndex / Semantic Kernel


Token limits, context window, memory


RAG (Retrieval Augmented Generation)


Function calling & agents


Building chatbots, copilots, assistants


Recommended Resources:

OpenAI API docs: https://platform.openai.com/docs


LangChain documentation


Full Stack LLM Course


📊 4. Data Science/ML Path: Model Customization & Training

🔵 Goal: Learn to fine-tune, evaluate, and deploy Gen AI models.


Topics:

Transformers & attention


Hugging Face 🤗 ecosystem (datasets, models, spaces)


Fine-tuning vs. instruction tuning vs. LoRA


Embeddings and vector databases (Pinecone, FAISS, Weaviate)


Evaluation: BLEU, ROUGE, hallucination detection


Tools:

PyTorch / TensorFlow


Hugging Face Transformers


Weights & Biases


LangSmith (for testing LLM apps)


🧠 5. Advanced Topics (All Roles)

Multi-modal models (GPT-4o, Gemini, Claude 3)


Agents & tools (OpenAI function calling, AutoGen, CrewAI)


Guardrails (e.g., prompt injection defense, moderation)


RLHF (Reinforcement Learning from Human Feedback)


Open source LLMs: Mistral, LLaMA, Mixtral


🧰 Tools You Should Learn Along the Way

Category Tools

Text Generation ChatGPT, Claude, Gemini, Mistral

Image/Video DALL·E, Midjourney, Runway, Sora

Code Assistants GitHub Copilot, Codeium, Cursor

Frameworks LangChain, LlamaIndex, Semantic Kernel

Vector DBs FAISS, Pinecone, Weaviate, Qdrant

Model Hosting Hugging Face Spaces, Replicate, AWS Bedrock, Vertex AI


🗂️ Sample Learning Plan (12 Weeks)

Week Focus Area Outcome

1–2 Core Gen AI concepts + prompting Use ChatGPT + create simple prompts

3–4 Build with OpenAI API Create a chatbot with memory

5–6 LangChain or LlamaIndex Add RAG and function calling

7–8 Embeddings + vector DBs Build a document Q&A assistant

9–10 Fine-tuning models (optional) Customize a model using Hugging Face

11–12 Capstone project Build a full-stack Gen AI app


🧑‍🎓 Where to Learn

📘 Courses:


DeepLearning.AI Gen AI Specialization (Coursera)


FastAI - NLP


OpenAI Cookbook


🧑‍💻 Communities:


r/LocalLLaMA


Hugging Face Discord


Twitter / YouTube creators like Harrison Chase, Andrej Karpathy


✅ Summary

Goal Learn This

Use Gen AI tools ChatGPT, DALL·E, Claude, Copilot

Build apps OpenAI API, LangChain, LlamaIndex

Customize models Hugging Face, PyTorch, fine-tuning

Scale and evaluate Vector DBs, eval metrics, prompt optimization


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