Artificial Intelligence

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

GenAI is evolving fast. You don’t need 50 courses or every framework. Master these 10 skills in sequence. 👇

1️⃣ Transformers & LLM Fundamentals
Learn transformer architecture, self-attention, Q/K/V, multi-head attention, masking, tokenization, context windows, positional encoding, and how LLMs generate responses.
📚 https://www.youtube.com/watch?v=ZhAz268Hdpw&t=784s

2️⃣ Embeddings & Vector Databases
Understand embeddings, semantic similarity, cosine similarity, nearest-neighbor search, vector indexing, and databases such as FAISS, Chroma, and Pinecone.
📚 Algorithms: https://www.youtube.com/watch?v=lvH_zPj2o04&t=156s
📚 Embeddings: https://www.youtube.com/watch?v=JsGwjwnl4hY
📚 Implementation: https://www.youtube.com/watch?v=5kCE0l11ZHk

3️⃣ Prompt Engineering & Structured Outputs
Learn zero-shot and few-shot prompting, system prompts, context engineering, JSON outputs, prompt optimization, tool calling, and validation.

4️⃣ Retrieval-Augmented Generation (RAG)
Master document ingestion, chunking, embeddings, semantic and hybrid retrieval, metadata filtering, reranking, grounded responses, citations, and evaluation.
📚 https://www.youtube.com/watch?v=4Ax77tnW0e8
📚 https://www.youtube.com/watch?v=X0btK9X0Xnk

5️⃣ LangChain
Connect models with data, tools, and application logic using integrations, prompt templates, retrievers, structured outputs, RAG pipelines, and workflows.
📚 https://www.youtube.com/watch?v=pSVk-5WemQ0&list=PLKnIA16_RmvaTbihpo4MtzVm4XOQa0ER0

6️⃣ LangGraph & Agentic AI
Build stateful workflows using nodes, edges, state management, conditional routing, memory, human approval, tool execution, error recovery, and multi-agent orchestration.
📚 https://www.youtube.com/watch?v=yC36gN-rqjo&list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL

7️⃣ Cloud AI & Production Deployment
Explore Microsoft Foundry, Amazon Bedrock, model hosting, inference APIs, enterprise RAG, agent deployment, observability, monitoring, scaling, cost optimization, and access control.
📚 Krish Naik: https://www.youtube.com/watch?v=ZP_U8eKBy2g
📚 Azure: https://www.youtube.com/watch?v=gKLEsHv8X6I&list=PLKN5-zCKJG7w-3gXIVptJV7sSi7fbQoBE
📚 AWS: https://www.youtube.com/watch?v=cTBGIKAckKE&t=880s

8️⃣ Evaluation, Security & Guardrails
Learn RAGAS, LLM-as-a-Judge, faithfulness, answer relevance, hallucination detection, prompt injection, data leakage, PII protection, testing, and regression evaluation.
📚 RAGAS: https://www.youtube.com/watch?v=J3M95E0LQl8
📚 Judge: https://www.youtube.com/watch?v=3FcYdRQPMCo
📚 Evaluations: https://www.youtube.com/watch?v=6W92_t9FveA&list=PLEneLIDJFpcA
📚 Injection: https://www.youtube.com/watch?v=DANRXYsT4Bs
📚 Guardrails: https://www.youtube.com/watch?v=7V1w5gnZ-kw

9️⃣ Model Context Protocol (MCP)
Understand Host, Client, Server architecture; tools, resources, prompts; transports; authentication; authorization; secure tool execution; and business-system integration.
📚 Anthropic: https://anthropic-partners.skilljar.com/introduction-to-model-context-protocol
📚 Gate Smashers: https://www.youtube.com/watch?v=GxHOSWxp7c0
📚 Chai aur Code: https://www.youtube.com/watch?v=dZyQNy3-HjU
📚 Krish Naik: https://www.youtube.com/watch?v=-UQ6OZywZ2I

🔟 Build Real-World GenAI Projects
Start with:
🛠 Post Generator: https://www.youtube.com/watch?v=qZ_J-Xg0QM4&list=PLeo1K3hjS3uvLuF4Tq1i1KqNM2QvIyzFK
🛠 LangGraph Chatbot: https://www.youtube.com/watch?v=51Ve2tE3Zns&list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL&index=10
🛠 Multi-Agent App: https://www.youtube.com/watch?v=_5XF5CCnbDk
Add authentication, logging, evaluation, monitoring, deployment, and a usable interface. That’s what turns a tutorial into a production-oriented application.

💡 Advice: Don’t chase every new framework. Master the fundamentals, build one complete application, deploy it, evaluate it, and improve it.
You don’t need 50 courses. You need the right concepts, sequence, and hands-on experience.

Lately, I've been interviewing AI Engineer candidates with 1–2 years of experience.

One thing surprised me.
Almost everyone can talk about RAG.
Almost everyone has built AI agents.

Most have used LangChain, LangGraph, OpenAI, Claude, or Gemini.
But when the conversation moves beyond frameworks...
...that's where many interviews start to slow down.

I ask simple questions like:
"Why do embeddings work?"
"Why does chunk size affect retrieval?"
"Why does an attention mechanism matter?"
"What actually happens before an LLM generates the first token?"

The room usually goes quiet.
It made me realize something.
We're getting really good at building AI applications.

But we're slowly skipping the fundamentals that make those applications reliable.
AI tools can write code.
They can generate APIs.
They can even scaffold an entire project.

But they can't replace your understanding.
If retrieval quality drops...
If latency suddenly doubles...
If hallucinations increase...
If your AI agent gets stuck in a loop...

The solution isn't another framework.
It's understanding what's happening underneath.
Frameworks will change.
Models will change.
The fundamentals won't.

That's why, before every interview, I'd rather revise concepts like:
→ Transformers
→ Attention Mechanism
→ Embeddings
→ Tokenization
→ Vector Search
→ HTTP & APIs
→ System Design
→ Memory
→ Databases
→ Evaluation
→ Python fundamentals
These are the concepts that help you explain why something works, not just how to build it.

If you're preparing for AI Engineer interviews, don't spend all your time learning the next framework.
Spend some time strengthening the foundation that every framework is built on.

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