🚀 Want to Become a Generative AI Engineer in 2026? Here's Your Complete Learning Roadmap.

Generative AI is evolving rapidly. But becoming a successful GenAI Engineer isn't about completing 50 courses or learning every new framework.

Here are 10 essential skills I recommend mastering, in the right order. 👇

1️⃣ Transformers & LLM Fundamentals

Start by understanding the architecture behind modern language models.

Learn:
• Transformer architecture and self-attention
• Query, Key, and Value (Q/K/V)
• Multi-head attention and masking
• Tokenization, context windows, and positional encoding
• How LLMs generate responses

📚 Codebasics:
https://www.youtube.com/watch?v=ZhAz268Hdpw&t=784s

2️⃣ Embeddings & Vector Databases

Understand how machines represent and retrieve semantic information.

Learn:
• Text embeddings and semantic similarity
• Cosine similarity, dot product, Euclidean distance
• Approximate nearest neighbor search
• Vector indexing and similarity search
• Vector databases such as FAISS, Chroma, and Pinecone

📚 Rohit Negi — Vector Database Algorithms:
https://www.youtube.com/watch?v=lvH_zPj2o04&t=156s

📚 Rohit Negi — Vector Embeddings:
https://www.youtube.com/watch?v=JsGwjwnl4hY

📚 Satyajit Pattnaik — Practical Implementation:
https://www.youtube.com/watch?v=5kCE0l11ZHk

3️⃣ Prompt Engineering & Structured Outputs

Prompt engineering is not just about writing better instructions. It's about designing reliable interactions with language models.

Learn:
• Zero-shot and few-shot prompting
• System prompts and context engineering
• Structured JSON outputs
• Prompt templates and optimization
• Tool calling and validation

4️⃣ Retrieval-Augmented Generation (RAG)

RAG is one of the most practical approaches for building enterprise GenAI applications.

Learn:
• Document ingestion and chunking
• Embedding generation
• Semantic and hybrid retrieval
• Metadata filtering and reranking
• Grounded response generation
• Citation handling and retrieval evaluation

📚 RAG Tutorial 1:
https://www.youtube.com/watch?v=4Ax77tnW0e8

📚 RAG Tutorial 2:
https://www.youtube.com/watch?v=X0btK9X0Xnk

5️⃣ LangChain

Learn how to connect language models with data, tools, and application logic.

Focus on:
• Model integrations
• Prompt templates
• Retrievers and tools
• Structured outputs
• RAG pipelines and application workflows

📚 CampusX:
https://www.youtube.com/watch?v=pSVk-5WemQ0&list=PLKnIA16_RmvaTbihpo4MtzVm4XOQa0ER0

6️⃣ LangGraph & Agentic AI

Move beyond simple chatbots and learn to build stateful AI workflows.

Learn:
• Nodes, edges, and state management
• Conditional routing
• Persistence and memory
• Human-in-the-loop approval
• Tool execution and error recovery
• Multi-agent orchestration

📚 CampusX:
https://www.youtube.com/watch?v=yC36gN-rqjo&list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL

7️⃣ Cloud AI Platforms & Production Deployment

A GenAI application becomes commercially valuable when it can run reliably in production.

Explore:
• Microsoft Foundry (formerly Azure AI Foundry)
• Amazon Bedrock
• Model hosting and inference APIs
• Enterprise RAG and agent deployment
• Observability, tracing, and monitoring
• Cost optimization, scaling, and access control

📚 Krish Naik:
https://www.youtube.com/watch?v=ZP_U8eKBy2g

📚 Azure AI Foundry:
https://www.youtube.com/watch?v=gKLEsHv8X6I&list=PLKN5-zCKJG7w-3gXIVptJV7sSi7fbQoBE

📚 AWS Bedrock:
https://www.youtube.com/watch?v=cTBGIKAckKE&t=880s

8️⃣ LLM Evaluation, Security & Guardrails

Building an AI demo is easy. Building an AI system that enterprises can trust is much harder.

Learn:
• RAGAS and retrieval evaluation
• LLM-as-a-Judge
• Faithfulness and answer relevance
• Hallucination detection
• Prompt injection and data leakage risks
• PII protection and guardrails
• Testing, monitoring, and regression evaluation

📚 RAGAS:
https://www.youtube.com/watch?v=J3M95E0LQl8

📚 LLM-as-a-Judge:
https://www.youtube.com/watch?v=3FcYdRQPMCo

📚 LLM Evaluations:
https://www.youtube.com/watch?v=6W92_t9FveA&list=PLEneLIDJFpcA

📚 Prompt Injection:
https://www.youtube.com/watch?v=DANRXYsT4Bs

🦖MoCap Anything V2 is out🦖

👉End-to-end motion capture for arbitrary skeletons: from a monocular video plus a reference asset straight to BVH-ready joint rotations, with no analytical IK. Works on humans, animals and custom rigs. Repo & weights under MIT, but RMBG-1.4 background remover is non-commercial💙

👉Review https://lnkd.in/p/dtZjGwh7
👉Paper https://lnkd.in/dG-dtCFT
👉Project https://lnkd.in/d7aGbYPj
👉Repo https://lnkd.in/dRWZNKjY

• Sent via TeleFeed

Boosting AI agent performance tenfold and forcing them to follow the plan without question — no hallucinations or made-up stuff.

Spec Kit has been released — it helps you think through the architecture, sets up AI agents to work according to a plan, so you get the correct result.

• Agents start to clearly build the architecture, break work into subtasks, and follow the plan.
• Works with Claude Code, Codex, Cursor, GitHub Copilot, and other agents.
• Fully open source.
• Free.

Use the tool — here.
Brain Storm

• Sent via TeleFeed