Generative AI & LLM Interview Guide for Top Roles (2025)

🧬

Explore the cutting-edge of artificial intelligence. This guide covers the state-of-the-art models and techniques, from foundational concepts in GANs and RL to the deep inner workings of Large Language Models.

🚀 Click to view detailed learning roadmaps

This path provides a broad understanding of the different families of generative models. It’s designed for someone who wants to grasp the entire landscape, not just language models.

Phase 1: The Foundations

Before tackling GenAI, ensure you have a solid grasp of Core AI Foundations and Machine Learning. These concepts are the bedrock upon which everything else is built.

Key Topics: Linear Algebra, Probability, Calculus, Neural Networks, Backpropagation, CNNs.

Phase 2: Transformer Fundamentals

This is the architectural breakthrough that enabled modern GenAI. Master the core mechanism first.

Dive Deep: Start with a detailed breakdown of the architecture in Transformers from Scratch.

Phase 3: Classic Generative Models

Learn the principles of latent spaces and adversarial training with models that paved the way for modern systems.

Explore: Understand the competing network dynamics in Generative Adversarial Networks (GANs).

Phase 4: Modern Image Generation

While LLMs dominate text, Diffusion models are the current state-of-the-art for image synthesis.

Master the Technique: Learn the denoising process in Diffusion Models to understand how models like Stable Diffusion and Midjourney work.

Phase 5: Learning to Act

Grasp the fundamentals of how an agent learns to make decisions to maximize rewards, a key concept for aligning advanced models.

Study the Basics: Cover Q-Learning and Policy Gradients in Reinforcement Learning.

This path is a deep dive specifically for mastering LLMs. It focuses entirely on the architecture, training, and application of modern language models.

Phase 1: Core Concepts

The goal here is to become an expert on the building blocks of all LLMs, from their architecture to how they process text.

Start Here: Deconstruct the architecture, tokenization, and embedding strategies in LLM Core Concepts.

Phase 2: Training and Alignment

A base model isn’t a helpful assistant. This phase covers the industrial-scale process of pretraining and the crucial alignment techniques that follow.

Learn the Lifecycle: Study pretraining, SFT, RLHF, and PEFT in Training & Fine-Tuning.

Phase 3: Application and Augmentation

This phase focuses on how to effectively use and augment a pre-trained LLM in a real-world application.

Elicit Intelligence: Master techniques in Prompting & Reasoning.
Add External Knowledge: Build systems that connect LLMs to your data with Retrieval-Augmented Generation (RAG).

Phase 4: The Frontier

This is the cutting edge, where LLMs are given tools, memory, and goals to act autonomously to solve complex problems.

Build Autonomous Systems: Explore ReAct, tool use, and planning in Agents & Autonomy.

Phase 5: The Ecosystem

Get hands-on by learning the essential libraries, frameworks, and services used to build and serve LLM-powered applications.

Master the Tools: Get familiar with the landscape in Tooling & Frameworks.

🔩 General Generative AI

What’s covered here?

This section explores the diverse landscape of generative models beyond just language. It covers foundational architectures for generating novel data, from the building blocks of Transformers to the adversarial dynamics of GANs and the decision-making processes of Reinforcement Learning.

Understand the fundamental components that make the Transformer architecture revolutionary.

Scaled Dot-Product Attention

The core mechanism for relating tokens.

Positional Encodings

Giving the model a sense of sequence order.

Multi-Head Self Attention

Allowing attention to focus on multiple aspects.

Apply transformers to solve common NLP tasks using the HuggingFace ecosystem.

Sentiment Classification

Classifying text as positive or negative.

Named Entity Recognition

Identifying entities like names and places.

Question Answering

Extracting answers from a context.

Explore GANs, where a Generator and Discriminator network compete to create realistic data.

Generator vs Discriminator

The two competing networks in a GAN.

Mode Collapse

A common failure mode in GAN training.

Minimax Loss Function

The objective function that drives GAN training.

Learn how agents make decisions in an environment to maximize cumulative rewards.

Q-Learning

A model-free, off-policy RL algorithm.

SARSA

An on-policy alternative to Q-Learning.

Bellman Equation

The fundamental equation for value functions.

Deep Q-Network (DQN)

Using neural networks for Q-values.

Policy vs. Value-Based

Two primary approaches to solving RL problems.

🧠 Large Language Models

What’s covered here?

This section is a deep dive into Large Language Models. It covers everything from their core architecture and training procedures to advanced applications like Retrieval-Augmented Generation (RAG) and autonomous agents.