IVQA 251-300

251. How can GenAI be applied in legal contract review automation?

  • Extracts clauses, risks, obligations

  • Highlights anomalies (e.g., missing indemnities)

  • Summarizes long contracts for quick review

  • Compares multiple versions for redlines Tools: Lexion, Harvey AI, Spellbook

252. What are the risks of using GenAI in healthcare diagnostics?

  • Hallucinations can lead to unsafe recommendations

  • Training bias → misdiagnosis

  • Regulatory liability (HIPAA, FDA)

  • Requires human oversight and traceable outputs

253. How do GenAI models support financial forecasting?

  • Extract market signals from reports, earnings calls

  • Summarize trends from unstructured financial documents

  • Assist with scenario modeling via simulation

  • Augment (not replace) traditional statistical models

254. What are the benefits of GenAI in e-commerce personalization?

  • Generates custom product descriptions and recommendations

  • Powers chatbots for guided shopping

  • Predicts buyer intent via conversation analysis

  • Dynamically adjusts homepage or ad content

255. How can GenAI improve supply chain visibility?

  • Summarizes logistics documents, invoices, bills of lading

  • Answers queries over ERP datasets

  • Detects anomalies and delays in real time

  • Enables natural language interfaces over dashboards

256. How do you use LLMs to enhance cybersecurity monitoring?

  • Summarize logs and alerts

  • Automate triage and analyst workflows

  • Translate raw data into human-readable explanations

  • Identify attack patterns using prompt-based detection

257. How can GenAI assist in internal company knowledge search?

  • Use RAG to query wikis, HR docs, and SOPs

  • Summarize long PDFs or knowledge base articles

  • Auto-tag and classify documents by topic

  • Integrate with Slack/Teams bots for natural queries

258. What are GenAI’s implications for journalism and media generation?

  • Drafts headlines, articles, or summaries

  • Suggests data visualizations

  • Automates translation and localization

  • Raises concerns around misinformation and authorship

259. How is GenAI used in customer journey orchestration?

  • Adapts responses across touchpoints (web, email, chat)

  • Provides context-aware next-best actions

  • Summarizes session behavior for agents

  • Informs segmentation and personalization in real time

260. How can GenAI automate compliance document generation?

  • Generate templates for GDPR, SOC2, HIPAA documents

  • Fill policies based on company data

  • Summarize compliance gaps or audit findings

  • Validate structure using Guardrails or JSON schemas

261. How do you manage model versioning in a production GenAI system?

  • Use semantic versioning (e.g., v1.2.3)

  • Track in metadata logs

  • Route traffic via versioned API endpoints

  • Include version tags in audit logs and responses

262. What tools do you use to monitor drift in GenAI model performance?

  • Evidently, Arize, WhyLabs for distribution shift

  • Custom logs on response latency, quality, satisfaction

  • Compare embedding distributions or classification accuracy over time

263. How do you update and roll back prompt templates safely?

  • Version prompt templates in Git

  • Store test cases for validation

  • Use feature flags for rollout

  • Keep logs for rollback visibility

264. What’s the role of A/B testing in GenAI prompt tuning?

  • Evaluate multiple prompt variants in production

  • Measure performance via user engagement or accuracy

  • Identify prompts that optimize cost/quality tradeoff

265. How do you manage dependency changes in LangChain or LlamaIndex apps?

  • Pin versions in requirements.txt or pyproject.toml

  • Use CI to test integration changes

  • Write modular wrapper layers for tool abstraction

266. What is your CI/CD pipeline for GenAI model deployments?

  • Lint + test code and prompts

  • Run eval suites on staging

  • Containerize and deploy models via Docker/Kubernetes

  • Track deployment via GitOps or model registry

267. How do you manage prompt logs and traceability for audit purposes?

  • Log prompt, output, timestamp, and model version

  • Use hashed user IDs to preserve privacy

  • Store in secure log systems (e.g., Loki, CloudTrail)

268. How do you fine-tune vs. swap models in response to product needs?

  • Fine-tune for domain-specific vocab or behavior

  • Swap when costs/latency/accuracy demand a new base model

  • Use embedding similarity to compare new vs. old model

269. How do you handle sunset of outdated LLM versions in production?

  • Notify users via dashboards

  • Freeze traffic and route to new version

  • Archive model logs and outputs for compliance

270. How do you make sure embedded vector data remains fresh over time?

  • Periodically re-embed stale documents

  • Store embedding version in vector DB

  • Use hashing to detect content changes

  • Retrain or migrate when switching embedding models

271. What are SSMs (State Space Models) and how are they replacing Transformers?

  • Replace attention with linear recurrence over time

  • Better for long sequences (e.g., 1M tokens)

  • Examples: Mamba, RWKV

  • Lower latency and memory usage

272. How does the RWKV architecture work?

  • Combines RNN and Transformer ideas

  • Uses time-mixing and channel-mixing to retain context

  • Enables linear scaling with sequence length

  • Efficient for training on long text

273. What are Retrieval-augmented Mixture of Experts (RMoE)?

  • Combine MoE routing with document retrieval

  • Each expert processes different retrieved chunks

  • Increases specialization while maintaining relevance

  • Balances computation with context

274. Explain the concept of toolformer models.

  • Models that learn when and how to use tools (e.g., calculators, search APIs)

  • Self-annotate training data with tool usage examples

  • Train via supervised learning + tool interaction traces

275. How do “language agents with memory graphs” improve GenAI reasoning?

  • Store knowledge as nodes + relations (graph memory)

  • Navigate and update graph over time

  • Enables symbolic reasoning + neural fluency

  • Promising for long-horizon tasks

276. What is the idea behind multi-agent collaborative LLMs?

  • Multiple LLMs take on specialized roles (e.g., coder, critic)

  • Communicate via structured messages

  • Perform reasoning, planning, critique in loop

  • Example: AutoGen, ChatDev

277. What is synthetic gradient and how does it speed up training?

  • Predicts gradient for a layer without waiting for backprop

  • Allows asynchronous or parallel layer training

  • Reduces latency and enables pipelined updates

278. How is GenAI being applied in neuro-symbolic reasoning?

  • Combine LLMs with logical reasoning engines

  • Use LLMs to generate candidate rules, then apply symbolic logic

  • Improve factuality and traceability

  • Applications: theorem proving, structured reasoning

279. What’s the role of instruction-following datasets in LLM performance?

  • Teach LLMs how to generalize across unseen tasks

  • Examples: FLAN, Self-Instruct, OpenHermes

  • Crucial for zero-shot performance and safety

280. How are long-context models like Claude 3, Gemini 1.5 or LLaMA 3 changing interaction design?

  • Support full-document inputs (up to 1M tokens)

  • Enable persistent memory and deep RAG

  • Shift UX from short Q&A to conversational agents with context history

281. How do you design user interfaces for GenAI assistants?

  • Use chat-based UI with clear input/output boundaries

  • Include source attribution, retry, and edit options

  • Show memory or context being used

  • Design for fallback and escalation

282. What’s the role of uncertainty estimation in GenAI UX?

  • Helps users calibrate trust in responses

  • Surface low-confidence flags visually

  • Improves decision-making in critical domains

  • Can be estimated via entropy or Monte Carlo sampling

283. How do you show citations and source confidence in RAG systems?

  • Link text spans to document sources

  • Show relevance scores

  • Allow users to expand and read context

  • Optionally include retrieval highlights

284. How do you reduce cognitive load in GenAI UI outputs?

  • Use bullet points, summaries, and visual structure

  • Minimize verbosity and repetition

  • Surface only top relevant responses

  • Offer expandable details (“Show more”)

285. How do you implement “Ask me anything” with guardrails?

  • Use moderation APIs to check inputs

  • Whitelist or pattern match for safe queries

  • Redirect unsafe queries to fallback responses

  • Log and monitor usage

286. What are good ways to let users correct GenAI outputs?

  • Inline editing with feedback loop

  • Thumbs up/down with comments

  • Allow regeneration or rephrasing

  • Use corrections to retrain or re-rank responses

287. How can you measure UX friction in LLM-generated responses?

  • Track metrics like re-tries, time to complete task, scroll depth

  • Use session recordings

  • Analyze feedback or bounce rates

  • Deploy UX surveys

288. How do you manage expectations around GenAI creativity vs. factuality?

  • Let users select response mode (“creative” vs. “factual”)

  • Add UI toggles for temperature and tone

  • Use disclaimers for generative outputs

  • Separate knowledge-based vs. freeform tasks

289. How do you provide “Explain this” interactions to build user trust?

  • Add a button to trigger explanation generation

  • Use Chain-of-Thought reasoning prompts

  • Highlight key decision points

  • Optionally include source or rule traces

290. How would you handle fallback when LLM fails to answer?

  • Provide friendly error or “I don’t know” messages

  • Offer suggestions or alternatives

  • Escalate to human agent or traditional FAQ

  • Use prompt rewriting and retry mechanism

291. How do you enforce data retention limits in a GenAI workflow?

  • Set TTL for logs and memory entries

  • Use time-bound vector DB policies

  • Auto-purge chat history after N days

  • Include metadata for expiry

292. What is differential privacy and how does it relate to LLMs?

  • Adds statistical noise to hide individual data points

  • Can be applied during model training or analytics

  • Prevents data leakage or membership inference

  • Used in privacy-preserving fine-tuning

293. How do you redact sensitive data before feeding it into prompts?

  • Use regex or NER models (e.g., spaCy, Presidio)

  • Replace PII with placeholders ([NAME], [EMAIL])

  • Validate redaction via human or secondary pass

294. What are your steps for responding to a data subject access request (DSAR)?

  • Identify all data tied to the subject

  • Search logs, memory stores, vector DBs

  • Provide readable export

  • Erase data if requested

295. What are AI Bill of Rights principles and how do they affect GenAI?

Key principles:

  • Safe and effective systems

  • Algorithmic discrimination protections

  • Data privacy

  • Notice and explanation

  • Human alternatives and fallback

These inform GenAI design for fairness and accountability.

296. What are the top compliance standards relevant to GenAI deployment (e.g., HIPAA, SOC 2)?

  • HIPAA (healthcare privacy)

  • SOC 2 (data security)

  • GDPR/CCPA (user rights)

  • ISO 27001 (info sec) Applies to both hosted and self-managed GenAI apps

297. How do you perform third-party model risk assessments?

  • Review model source, training data, biases

  • Check licensing terms

  • Test for unsafe behavior or drift

  • Maintain supplier risk logs

298. What are model cards and why are they important in AI governance?

  • Document model capabilities, limitations, risks

  • Include training data, performance, intended use

  • Required for responsible deployment

  • Improve transparency and trust

299. What’s your incident response plan for GenAI misuse or harm?

  • Real-time monitoring and alerts

  • Disable offending endpoint or feature

  • Notify affected users

  • Perform post-mortem and retrain or patch

300. What’s the difference between model transparency and explainability?

  • Transparency: Disclosing how the model was built (data, training)

  • Explainability: Making outputs understandable to humans Both are key for trust and regulatory compliance

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