One common mistake I see among people entering the AI field:
They make an API call to an LLM, get a response, build a small application, and think they understand how LLMs work.
But that’s only the surface.
The real learning starts when you begin asking:
→ What is actually happening inside a neural network?
→ Why are GPUs so important for AI?
→ How does a Transformer work?
→ What are attention and tokenization?
→ How does training actually happen?
→ How do you build and train a language model yourself?
These are also the kinds of fundamentals that can separate you in a serious AI/GenAI interview. That’s exactly why I wrote:
📕 Building A Small Language Model from Scratch: A Practical Guide
It’s an 854-page practical journey that starts with the fundamentals: Neural Networks, GPUs, Transformers, tokenization, and attention, and gradually takes you toward building your own language model from scratch.
If you want to go deeper than simply calling an API, the book is still available:
📘 Gumroad: https://plakhera.gumroad.com/l/BuildingASmallLanguageModelfromScratch
📘 Leanpub: https://leanpub.com/buildingasmalllanguagemodelfromscratch
📘 Amazon: https://www.amazon.com/Building-Small-Language-Model-Scratch-ebook/dp/B0G64SQ4F8/
🚀 Looking for a structured interview-preparation path?
I’m also running a 3-month program:
Cracking the GenAI Interview for DevOps, SRE, Platform & Forward-Deployed Engineers
The program focuses on:
→ Generative AI & LLM Engineering
→ Python & DSA
→ System Design
→ DevOps/SRE Automation with AI
→ Hands-on AI projects
→ Interview-focused preparation
📌 Morning Batch: https://www.ideaweaver.ai/purchase?product_id=6827463
📌 Evening Batch: https://www.ideaweaver.ai/purchase?product_id=6827464
📌 Self-Paced Batch: https://www.ideaweaver.ai/purchase?product_id=6827466
📌 Complete Program Details: https://www.ideaweaver.ai/courses/cracking-the-genai-interview-for-devops-sre-platform-forward-deployed-engineers-morning1/lectures/66591133
Don’t just learn how to call an LLM. Learn what is happening underneath it.
