Here’s Section 2: Gen AI & AI Tools for the demo interview. This section is designed for ~1 hour with 12 questions (conceptual + practical + scenario-based). Each question includes detailed sample answers.
✅ Section 2: Gen AI & AI Tools
Topic Goals
- Assess understanding of Generative AI concepts, LLM testing, and AI toolchains.
- Evaluate ability to design QA strategies for AI systems.
- Check familiarity with benchmarking, prompt engineering, and model evaluation.
Q1. What is Generative AI, and how is it different from traditional AI?
Sample Answer:
- Generative AI creates new content (text, images, code) using models like GPT, Stable Diffusion.
- Traditional AI focuses on classification, prediction, or decision-making based on existing data.
- Key difference: Generative AI learns data distribution and generates novel outputs.
Q2. Explain the concept of Large Language Models (LLMs).
Sample Answer:
- LLMs are deep learning models trained on massive text corpora to predict the next token in a sequence.
- Examples: GPT, LLaMA, Claude.
- Key features: Transformer architecture, attention mechanism, scaling laws.
Q3. How do you test an LLM-based application?
Sample Answer:
- Functional Testing: Validate prompt → response correctness.
- Robustness Testing: Test adversarial prompts, edge cases.
- Bias & Fairness Testing: Check for harmful or biased outputs.
- Performance Testing: Latency, throughput under load.
- Evaluation Metrics: BLEU, ROUGE, perplexity, human evaluation.
Q4. What is prompt engineering, and why is it important?
Sample Answer:
- Prompt engineering is crafting input prompts to guide model behavior.
- Important because LLMs are context-sensitive; small changes in prompts can drastically affect output.
- Techniques: Few-shot, Chain-of-Thought, Role prompting.
Q5. How do you benchmark an AI model?
Sample Answer:
- Use standard datasets (e.g., GLUE, MMLU for NLP).
- Metrics: Accuracy, F1-score, BLEU, Latency, Memory footprint.
- Compare against baseline models and SOTA benchmarks.
Q6. What are common failure modes of LLMs?
Sample Answer:
- Hallucination: Fabricating facts.
- Prompt sensitivity: Output changes drastically with minor prompt changes.
- Bias & Toxicity: Producing harmful content.
- Context window limits: Forgetting earlier context in long conversations.
Q7. How do you ensure reproducibility in AI experiments?
Sample Answer:
- Fix random seeds.
- Log hyperparameters, model versions, dataset versions.
- Use tools like MLflow, Weights & Biases.
- Containerize environment with Docker.
Q8. Explain the difference between fine-tuning and prompt-tuning.
Sample Answer:
- Fine-tuning: Retraining model weights on domain-specific data.
- Prompt-tuning: Adding small trainable parameters (soft prompts) without changing core weights.
- Prompt-tuning is lighter and faster.
Q9. Scenario: Your AI model gives inconsistent answers for the same question. How do you debug?
Sample Answer:
- Check temperature parameter (controls randomness).
- Ensure deterministic settings (seed, sampling).
- Analyze prompt ambiguity.
- Validate model version and deployment consistency.
Q10. Scenario: You need to test an AI chatbot for safety. What’s your approach?
Sample Answer:
- Create adversarial prompts (jailbreak attempts).
- Test for harmful content, bias, privacy leaks.
- Use red-teaming and toxicity detection tools (Perspective API).
- Implement guardrails (e.g., moderation filters).
Q11. Which tools or frameworks would you use for LLM evaluation?
Sample Answer:
- OpenAI Evals, LangChain Benchmarks, EleutherAI Harness.
- For metrics: BLEU, ROUGE, BERTScore.
- For human eval: Crowdsourcing platforms (MTurk).
Q12. How would you integrate AI testing into a CI/CD pipeline?
Sample Answer:
- Containerize model and test scripts with Docker.
- Use pytest for functional tests.
- Automate model performance checks (latency, accuracy) in CI.
- Deploy with Kubernetes for scalability.
✅ Time Allocation:
- Concepts (Q1–Q5): ~20 mins
- Failure modes & reproducibility (Q6–Q8): ~15 mins
- Scenarios (Q9–Q12): ~25 mins
👉 Do you want me to move on to Section 3: Automation Framework & Programming & K8s now, or should I add a quick difficulty rating and expected senior-level performance notes for Section 2 before proceeding?