Generative AI
Interviews & Career Path
Everything you need to land your dream role in the GenAI ecosystem — from foundational concepts to salary negotiations and beyond.
Foundational Knowledge
Core concepts every GenAI candidate must master
Transformer Architecture
Understand self-attention, multi-head attention, positional encodings, and the encoder-decoder structure. Be ready to explain how tokens, embeddings, and the softmax output work end-to-end.
Training Paradigms
Pre-training (next-token prediction), instruction fine-tuning (SFT), RLHF, and DPO. Know the difference between zero-shot, few-shot, and chain-of-thought prompting.
Context & Memory
Context window limits, KV-cache mechanics, positional encoding strategies (RoPE, ALiBi), and retrieval-augmented generation (RAG) as an alternative to long-context models.
Evaluation & Benchmarks
MMLU, HumanEval, HellaSwag, TruthfulQA — how they’re constructed and what they measure. Know about hallucination, factuality metrics, and human preference evaluations.
Scaling Laws
Chinchilla scaling laws, the relationship between model size, training tokens, and compute budget. Why bigger isn’t always better and how efficiency improvements (MoE, speculative decoding) change the equation.
Multimodal Models
Vision-language models (CLIP, LLaVA), image tokenization (VQ-VAE, patch embeddings), speech-to-text and text-to-speech pipelines, and cross-modal attention mechanisms.
Common Interview Questions
Frequently asked across technical and product GenAI roles
Q What is the difference between RAG and fine-tuning?
RAG retrieves external documents at inference time and injects them into the prompt; ideal for up-to-date, factual tasks. Fine-tuning bakes knowledge into weights; better for style, format, and latency-sensitive workloads where retrieval is costly.
Q How does temperature affect generation?
Temperature scales the logits before softmax: low values (<0.5) make the model more deterministic and peaked; high values (>1.0) flatten the distribution, increasing diversity but risking incoherence. Top-p (nucleus) sampling is often used alongside it.
Q Explain catastrophic forgetting and how to mitigate it.
When fine-tuning on new data, the model overwrites weights used for old tasks. Mitigations include: LoRA/QLoRA (adapter-only training), elastic weight consolidation (EWC), replay buffers, and parameter-efficient continual learning strategies.
Q What are guardrails and how do you implement them?
Guardrails prevent harmful, off-topic, or policy-violating outputs. Implementation layers: system prompts, input/output classifiers, constitutional AI alignment, prompt injection defenses, and human-in-the-loop review for high-risk workflows.
Q Walk me through building a production RAG pipeline.
Chunk and embed documents → store in vector DB (Pinecone, Weaviate, pgvector) → at query time embed the question, retrieve top-k chunks, re-rank with a cross-encoder → inject into LLM prompt with citation tracking and hallucination checks.
Q How would you reduce inference costs by 10×?
Quantization (INT8/INT4), speculative decoding, smaller distilled models, batching strategies, caching frequent responses, prefix caching, and routing simple queries to cheaper models while reserving large models for complex tasks.
GenAI Career Paths
Where you can go and what each role demands
AI/ML Engineer
Build and deploy LLM-powered systems. Owns the full stack from model selection to production inference infrastructure and evaluation harnesses.
Research Scientist
Advances the state-of-the-art in model capabilities, alignment, or efficiency. Requires strong mathematical foundations and publication record.
Prompt Engineer
Designs, tests, and iterates on prompts and system instructions to extract optimal behaviour from foundation models without touching weights.
AI Product Manager
Bridges technical teams and business goals for AI products. Defines success metrics, manages model risk, and navigates rapid capability changes.
AI Safety Engineer
Evaluates model risks, builds red-teaming infrastructure, implements content policies, and ensures alignment with constitutional AI principles.
MLOps / LLMOps
Owns model serving, observability, prompt versioning, cost monitoring, and the CI/CD pipelines that keep GenAI products reliable at scale.
Interview Preparation Tips
Strategies that actually move the needle
Recommended Resources
Curated learning paths by role
📚 Must-Read Papers
- Attention Is All You Need (Vaswani et al., 2017)
- Training language models to follow instructions (InstructGPT)
- Constitutional AI (Anthropic, 2022)
- Chinchilla: Training Compute-Optimal LLMs
- RAG: Retrieval-Augmented Generation for NLP Tasks
- LoRA: Low-Rank Adaptation of Large Language Models
🛠️ Hands-On Practice
- Hugging Face — model hub, courses, and spaces
- LangChain / LlamaIndex — build RAG pipelines
- OpenAI Cookbook — real prompt engineering examples
- Weights & Biases — experiment tracking course
- fast.ai Practical Deep Learning — free & excellent
- Andrej Karpathy — Neural Networks: Zero to Hero
🎓 Courses & Certifications
- DeepLearning.AI — Short Courses on LLMs & RAG
- Stanford CS324 — Large Language Models
- AWS / GCP / Azure GenAI Certifications
- Coursera — ML Specialization (Andrew Ng)
- MIT 6.S965 — TinyML and Efficient Deep Learning
💼 Where to Look for Roles
- Anthropic, OpenAI, Google DeepMind, Meta AI
- YC startups — airtable.com/ycombinator jobs board
- LinkedIn — filter “Generative AI” + open to work
- Hugging Face Jobs — curated ML roles
- ML Safety Newsletter & 80,000 Hours (safety focus)
- Remote-first boards: Contra, Arc, Wellfound
