✅ Generative AI Interview Topics & Learning Checklist
Polished, interview-ready blog post for AI / ML / Data Science professionals preparing for LLM and generative AI roles.
Introduction
Generative AI has shifted from research labs into production systems that power chatbots, image studios, code assistants, and more. For candidates interviewing for roles in AI, ML, Data Science, or LLM-focused development, recruiters expect a mix of theoretical knowledge and hands-on experience. This blog post is a compact, practical guide — both a learning checklist and an interview prep playbook — covering the must-know topics, sample answers, and suggested projects that will help you stand out.
🔹 Basics of Generative AI
What to know:
Definition: Generative AI refers to models that can produce new data (text, images, audio, video) that resembles the training distribution.
Generative vs Traditional AI: Traditional AI often focuses on classification, regression, or rule-based systems. Generative AI focuses on creation and often requires large-scale unsupervised or self-supervised learning.
Applications: Content creation (articles, marketing copy), image & video generation, code synthesis, personalized tutoring, speech synthesis, and more.
Ethics & Safety: Understand bias, hallucinations, data misuse, privacy concerns, and the importance of guardrails.
Sample interview answer: “Generative AI produces new content by modeling data distributions; its risks include generating plausible-sounding but false information (hallucinations) and amplifying biases unless carefully curated and evaluated.”
🔹 Core Concepts
Topics to master:
Language Models (LMs) and their capabilities
Transformers architecture and the role of positional encodings
Self-attention mechanism and how it enables context-aware representation
Tokens, tokenization strategies, and embeddings
Autoregressive (GPT-style) vs autoencoder/BERT-style models
When to fine-tune vs when to rely on prompt engineering
Quick tip: Draw diagrams for transformers and attention flow — they’re commonly asked on whiteboards.
🔹 Popular Generative Models
Know the differences & use-cases:
GPT family: Autoregressive LMs great for fluent text generation and instruction-following.
BERT: Bidirectional encoder suited for understanding tasks (classification, NER). Not directly for open-ended generation.
DALL·E, Stable Diffusion, Midjourney: Image generation — differences in architecture, speed, and control mechanisms.
Whisper: Speech-to-text — strengths in multi-lingual ASR.
Codex / Copilot: Code generation and assistant tools designed for developer workflows.
🔹 Training Techniques
Key methods to understand:
Pretraining & Transfer Learning: Large-scale pretraining followed by fine-tuning for downstream tasks.
RLHF (Reinforcement Learning from Human Feedback): Aligns model outputs with human preferences.
LoRA & PEFT: Techniques to adapt large models efficiently by tuning fewer parameters.
RAG (Retrieval-Augmented Generation): Combine retrieval systems with LMs to ground outputs in external knowledge.
🔹 Evaluation Metrics
What interviewers expect:
Text metrics: BLEU, ROUGE (and their limitations)
Image metrics: FID, IS (and reliance on human evaluation)
Importance of human-in-the-loop evaluation for fluency, relevance, and safety
Tools and tests for measuring toxicity and bias
🔹 Prompt Engineering
Practical prompts to learn:
Zero-shot, one-shot, few-shot prompting
Chain-of-Thought (CoT) prompting for complex reasoning tasks
Role prompts and system messages to control style/behavior
Tooling: frameworks like LangChain and LlamaIndex for building pipelines
Interview example: Explain CoT prompting and when it helps improve model reasoning.
🔹 Popular APIs & Platforms
Be familiar with:
OpenAI (ChatGPT, DALL·E, Whisper)
Google Gemini
Anthropic Claude
Meta LLaMA
Hugging Face Transformers
Microsoft Azure OpenAI
Practical task: Show how to call a small completion endpoint; explain rate limits, cost considerations, and latency trade-offs.
🔹 Real-world Applications
Common production scenarios:
Conversational agents and virtual assistants
Code generation, review, and automated pull requests
Long-form content generation and summarization
Domain-specific assistants (medical, legal) — with special emphasis on grounding and verifiability
Creative applications: AI art, music, and video
🔹 Sample Interview Q&A (Concise answers you can memorize)
Q: What is the transformer attention mechanism? A: The attention mechanism computes weighted sums of token representations so the model can focus on relevant parts of the input when producing a token’s representation. Scaled dot-product attention is the common implementation.
Q: When would you use RAG? A: Use RAG when you need factual, up-to-date, or domain-specific answers that are grounded in external sources to reduce hallucinations.
Q: What is RLHF and why is it used? A: RLHF uses human feedback to create a reward model; then reinforcement learning optimizes the language model to produce outputs that align with human preferences (better helpfulness and safety).
🔹 Mini Projects to Practice (Hands-on wins interviews)
Build a LangChain chatbot that uses RAG to answer company-specific FAQs.
Fine-tune a small model using LoRA/PEFT for a niche domain (e.g., medical notes tone).
Create an image generation prompt series and evaluate outputs using human judgment.
Comments
Post a Comment