GenAI Engineering

Complete interview preparation for GenAI Engineer, LLM Engineer, and Applied AI Scientist roles

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Overview

This section bridges the gap between ML/transformer theory and production GenAI systems. Whether you're preparing for interviews at OpenAI, Anthropic, Google DeepMind, or building LLM applications at startups, this guide covers everything you need.

Prerequisites

Before diving in, ensure you've completed:

• Transformers Deep Dive - Attention mechanisms, architecture, training
• ML Fundamentals - Core ML concepts
• Basic Python and PyTorch proficiency

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Learning Paths

Path 1: LLM Application Developer (4-6 weeks)

For engineers building products with LLM APIs

Week	Topics	Key Skills
1-2	LLM Foundations, Prompt Engineering	Sampling, CoT, few-shot
3-4	RAG Systems	Embeddings, chunking, retrieval
5	Agents & Tools	Function calling, ReAct
6	Evaluation, LLMOps	Metrics, deployment

Path 2: LLM/ML Engineer (6-8 weeks)

For engineers fine-tuning and deploying models

Week	Topics	Key Skills
1-2	LLM Foundations	Architecture, inference
3-4	Fine-Tuning, PEFT	LoRA, instruction tuning
5	RLHF/DPO, Evaluation	Preference learning, benchmarks
6-7	Safety, LLMOps	Guardrails, serving
8	System Design	End-to-end architectures

Path 3: Research Engineer (8+ weeks)

For researchers pushing the frontier

Week	Topics	Key Skills
1-2	LLM Foundations, Scaling	Architecture deep dive
3-4	Fine-Tuning, RLHF	Training dynamics
5-6	Safety, Constitutional AI	Alignment techniques
7-8	Multimodal, Advanced	Vision-language, video

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Topics

1. LLM Foundations

Bridge from transformer theory to practical LLM understanding.

Module	Description
How LLMs Actually Work	Inference pipeline, sampling strategies
Tokenization Deep Dive	BPE, SentencePiece, vocabulary
Scaling Laws	Chinchilla, emergent abilities
Model Families	GPT, Claude, Llama, Gemini
Context & Memory	Context windows, attention patterns
Inference Optimization	KV-cache, speculative decoding

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2. Prompt Engineering

The art and science of communicating with LLMs.

Module	Description
Prompt Anatomy	Roles, structure, delimiters
Few-Shot Learning	Example selection, ordering
Chain-of-Thought	CoT, self-consistency, ToT
Advanced Techniques	ReAct, decomposition
Prompt Optimization	APE, DSPy, A/B testing
Prompt Security	Injection, jailbreaks, defense
Prompt Evaluation	Metrics, versioning

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3. RAG Systems

Building retrieval-augmented generation pipelines.

Module	Description
RAG Architecture	E2E pipeline, decision framework
Embedding Models	OpenAI, Cohere, BGE, similarity
Vector Databases	Pinecone, Weaviate, HNSW
Chunking Strategies	Fixed, semantic, recursive
Retrieval Optimization	Hybrid search, reranking
Advanced RAG Patterns	FLARE, Self-RAG, GraphRAG
RAG Evaluation	RAGAS, MRR, faithfulness

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4. Fine-Tuning LLMs

When and how to adapt foundation models.

Module	Description
When to Fine-Tune	Decision framework
Full Fine-Tuning	Training, catastrophic forgetting
PEFT Methods	LoRA, QLoRA, adapters
Instruction Tuning	Dataset creation, FLAN
RLHF & DPO	Reward modeling, preferences
Data Preparation	Quality, synthetic data
Fine-Tuning Evaluation	Overfitting, benchmarks

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5. Agents & Tool Use

Building autonomous AI systems.

Module	Description
Agent Architectures	ReAct, MRKL, cognitive
Function Calling	Schemas, extraction
Multi-Agent Systems	Orchestration, debate
Memory & State	Short/long-term memory
Agent Frameworks	LangChain, AutoGen
Agent Evaluation	Task completion, safety

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6. Evaluation & Benchmarking

Measuring LLM capabilities systematically.

Module	Description
Evaluation Taxonomy	Categories, dimensions
Automated Metrics	BLEU, ROUGE, BERTScore
LLM-as-Judge	G-Eval, calibration
Human Evaluation	Annotation, agreement
Hallucination Detection	Detection, mitigation
Benchmarks	MMLU, HumanEval, MT-Bench

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7. Safety & Alignment

Building responsible AI systems.

Module	Description
Safety Fundamentals	Landscape, risks
Content Filtering	Moderation, PII
Guardrails	NeMo, validators
Red Teaming	Adversarial testing
Constitutional AI	Principle-based training
Responsible AI	Fairness, governance

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8. LLMOps & Production

Operating LLMs at scale.

Module	Description
Serving Infrastructure	vLLM, TGI, Triton
Cost Optimization	Token economics, caching
Latency Optimization	TTFT, streaming
Caching Strategies	Semantic, KV-cache
Monitoring	Metrics, observability
Scaling Patterns	Horizontal, auto-scaling

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9. Multimodal AI

Beyond text: vision, audio, and video.

Module	Description
Vision-Language Models	CLIP, GPT-4V, LLaVA
Image Generation	Diffusion, DALL-E
Audio Models	Whisper, TTS
Video Understanding	Video-LLMs, temporal
Multimodal RAG	Cross-modal retrieval

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10. GenAI System Design

End-to-end design for GenAI interviews.

Module	Description
Design Framework	Methodology, trade-offs
Chatbot Design	Multi-turn, personas
Enterprise RAG	Knowledge bases, scale
Code Assistant	Copilot architecture
Content Pipeline	Generation at scale
Interview Questions	15+ solved designs

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Quick Reference

Interview Question Bank

Category	Count	Difficulty
LLM Fundamentals	10-12	Medium
Prompt Engineering	12-15	Medium-Hard
RAG Systems	15-18	Hard
Fine-Tuning	12-15	Hard
Agents	10-12	Hard
Evaluation	10-12	Medium
Safety	8-10	Medium
LLMOps	10-12	Hard
Multimodal	8-10	Hard
System Design	15-20	Very Hard

Total: 100+ interview questions with detailed solutions

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Key Concepts Cheat Sheet

Concept	One-Liner
Temperature	Controls randomness in token sampling (0 = deterministic, 1+ = creative)
Top-k	Sample from k highest probability tokens
Top-p (Nucleus)	Sample from smallest set with cumulative prob >= p
CoT	Chain-of-thought: "Let's think step by step"
RAG	Retrieval-Augmented Generation: retrieve then generate
LoRA	Low-Rank Adaptation: efficient fine-tuning with small matrices
DPO	Direct Preference Optimization: simpler alternative to RLHF
ReAct	Reasoning + Acting: interleave thought and action
TTFT	Time To First Token: initial response latency
KV-Cache	Key-Value cache: stores attention computations

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Technology Landscape

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How to Use This Section

1. Pick your learning path based on your target role
2. Work through modules sequentially within each topic
3. Complete the interview Q&A at the end of each module
4. Build mini-projects to solidify understanding
5. Review system design before interviews

Interview Focus

Each module includes an "Interview Angle" section highlighting how concepts appear in real interviews. Pay special attention to these sections.

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Related Sections

Section	Connection
Transformers	Foundational architecture for all LLMs
ML System Design	General ML system design principles
ML Coding	Implementation practice

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Section created: January 2025Estimated completion time: 4-8 weeks depending on path