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Guides
Practical reads, project ideas, and interview guidance. Browse below, then join the live bootcamp when you want a structured roadmap.
Explore the papers and resources that shaped modern AI. From the Transformer architecture to GPT-3, chain-of-thought prompting, and the official LangChain and LangGraph docs, this curated reading list will help you build a stronger foundation as an AI engineer.
By Kinjal Tiwari · LinkedIn
Learn the key differences between LangChain and LangGraph, two powerful frameworks for building AI applications. Discover when to use simple chains, when to build autonomous agents, and how to choose the right tool for your workflow, scalability, and complexity needs.
By Kinjal Tiwari · LinkedIn
Why do some people get amazing results from AI while others don't? The difference is often prompt engineering. Learn how to guide LLMs, improve reasoning, enforce structure, and consistently generate better outputs using the techniques professionals rely on every day.
By Kinjal Tiwari · LinkedIn
Large Language Models
By Kinjal Tiwari · LinkedIn
Before you dive into building real-world AI systems, this primer gives you the mental models and vocabulary you need. No heavy math — just clear, intuitive explanations that will make everything else in the course click.
By Kinjal Tiwari · LinkedIn
Every tool category. What goes in each slot. Why we chose it over the alternatives. Built from 8 sprints of teaching engineers to ship real AI systems not demo projects.
By Pratyaksh Singh · LinkedIn
Tokens, context windows, and temperature — not as definitions, but as the mental models you'll use every time you write a prompt or debug a model call.
By Pratyaksh Singh · LinkedIn
Next step
Resources are a strong start. Join a live cohort to get structure, doubt-solving, and project reviews that help you move faster.