IVQA 351-400
351. How do you compare proprietary LLMs like GPT-4, Claude, Gemini, and Mistral for a given use case?
Evaluation Metrics: Accuracy, coherence, factuality, hallucination rate
Use Case Fit: Claude excels in long-context summarization, GPT-4 in code + creativity, Gemini in multi-modal tasks
APIs & Cost: Compare pricing and latency
Availability & Licensing: Mistral is open-source, others have usage restrictions
Benchmark using standardized datasets (e.g., MMLU, HELM)
352. What criteria would you use to choose between a 7B and a 70B model?
Task complexity (e.g., reasoning requires larger models)
Latency and infrastructure constraints
Cost per inference
Fine-tunability: 7B models are easier to customize and deploy locally
Accuracy vs. Speed trade-off
353. When is it better to use a distilled or quantized model instead of the full one?
When inference cost or device footprint is critical (edge, mobile)
For non-mission-critical tasks (e.g., auto-tagging, summarization)
If latency matters more than accuracy
During prototyping or offline batch processing
354. How do you benchmark multiple LLMs for summarization vs. generation?
Use datasets like CNN/DailyMail, XSum for summarization
For generation: creative writing prompts, story completion tasks
Metrics: ROUGE, BLEU, BERTScore, human eval
Run prompts across models in identical conditions (same input, decoding config)
355. What is the importance of context window in model selection?
Determines how much input (tokens) the model can “see”
Essential for tasks like document Q&A, summarization, multi-turn dialogue
Long context (e.g., Claude 3 or GPT-4-128k) enables memory-rich applications
356. How do you factor in inference speed when selecting a GenAI model?
Evaluate tokens/sec per dollar or per core
Batch processing capabilities (e.g., vLLM)
Consider latency budgets for user experience (e.g., <300ms for autocomplete)
Trade-off accuracy vs. response time based on use case
357. What are the trade-offs between open-source and proprietary LLMs?
Cost
Free/cheap to run
Pay-per-token
Control
Full
Limited
Performance
Less fine-tuned (varies)
Top-tier accuracy
Compliance
Better for data sovereignty
Harder for HIPAA, SOC2, etc.
358. How does finetuning affect model comparability?
Makes models domain-specific → harder to benchmark against generic ones
Alters response patterns (length, tone, accuracy)
Must compare using same downstream task and validation set
Fine-tuned models often overfit to training data if not carefully regularized
359. How do you run a fair A/B/C test across different LLMs in a production setting?
Randomly assign users or sessions to LLM variants
Control for prompt formatting, context length, decoding params
Track task completion, latency, cost, user satisfaction
Normalize for user base differences (e.g., region, query type)
360. What are the implications of model licensing (e.g., Apache 2.0 vs. non-commercial) in choosing LLMs?
Apache 2.0: Safe for commercial use, modifiable
Non-commercial: Limits monetization (e.g., LLaMA 2)
Custom Terms: Require legal vetting (e.g., Claude, Gemini)
Impacts go-to-market and deployment strategy
361. What is the difference between adversarial prompts and jailbreak prompts?
Adversarial: Cause unexpected or incorrect behavior (e.g., logic traps)
Jailbreak: Explicitly bypass safety filters (e.g., "ignore the above rules")
Both expose model vulnerabilities but target different attack vectors
362. How do you set up red teaming for a GenAI product before launch?
Define risk areas: bias, hallucination, safety, reliability
Create adversarial test prompts
Use internal teams or third-party red teamers
Log, classify, and patch model weaknesses
363. What are the most common failure modes of LLMs in production?
Hallucinations
Toxicity/bias
Jailbreaks
Prompt injection
Memory drift or state leakage
364. How do you test LLMs for bias, toxicity, and misinformation?
Use test suites (e.g., RealToxicityPrompts, BOLD, StereoSet)
Run counterfactual prompts (e.g., change gender, race)
Apply classification models or keyword filtering
Validate against knowledge graphs or fact-checkers
365. What datasets are used for LLM safety benchmarks?
TruthfulQA: Hallucination and misinformation
RealToxicityPrompts: Offensive content generation
HarmBench: Abuse of tools or policies
Bias in Bios: Gender bias in occupation inference
366. How would you design a human-in-the-loop review system for GenAI output moderation?
Triage risky outputs via classifier scores
Route borderline responses to moderators
Annotate with reason codes and feedback
Feed corrections back to tuning loop
367. How do you build internal reporting dashboards for misuse detection?
Track flagged prompts/responses over time
Breakdown by user, feature, or model version
Include top violations and intervention rate
Enable real-time alerts and review queues
368. What are effective thresholds for blocking vs. warning in unsafe outputs?
Blocking: High toxicity/profanity score (>0.9), illegal content
Warning: Medium-risk (0.6–0.9), unclear hallucinations
Calibrate thresholds using human-in-the-loop data and A/B testing
369. How do you measure effectiveness of safety interventions over time?
Decrease in incident rate
Increase in caught vs. missed edge cases
Model improvement in benchmark scores
Reduced moderator load
370. What is “red teaming-as-a-service” and how can it help scale GenAI safety testing?
External orgs specialize in testing LLMs for vulnerabilities
Provide diverse, real-world adversarial examples
Help validate safety claims for audits or compliance
Scales internal capacity without full-time staffing
371. How can you collect structured feedback on GenAI outputs?
Thumbs up/down + optional comments
Likert scale (“How helpful was this?”)
Task success signals (e.g., did user retry?)
Categorized feedback tags (bias, irrelevant, hallucination)
372. What are the pros and cons of thumbs-up/down systems for LLMs?
Simple to implement
Limited nuance
Real-time signal
Can be noisy or inconsistent
Easy to scale
Doesn’t capture why it was wrong
373. How do you use user corrections to improve a prompt template?
Extract patterns from corrected completions
Add few-shot examples to guide behavior
Refine instructions to avoid known missteps
Evaluate improved prompts on the same dataset
374. How do you incorporate feedback into prompt routing logic?
Tag prompts with metadata (intent, topic)
Track which prompts perform best per class
Use classifiers to route future inputs to optimal prompt-template or model
375. What is reinforcement learning from human feedback (RLHF), and when would you use it post-deployment?
Fine-tuning with reward scores from human preferences
Used when thumbs data or pairwise comparisons are available
Helpful for reducing hallucinations, improving helpfulness/harmlessness
376. How do you prevent “feedback poisoning” in open feedback systems?
Authenticate users
Rate-limit feedback submissions
Apply outlier detection or feedback validation heuristics
Filter based on user trust score
377. How do you distinguish between UX complaints and model behavior issues?
Tag feedback by type (UI vs. model)
Analyze session context (e.g., input lag vs. hallucination)
Segment feedback by location in the flow (model response vs. loading)
378. What are automated metrics that correlate with human preference?
BERTScore
MAUVE
GPT-4 voting or preference proxy
Task completion time or edit distance
379. How do you set up dashboards that track feedback over time by use case?
Use tools like Metabase, Superset, Grafana
Track feedback score by task type, prompt, model version
Enable filtering by user cohort or geography
Alert on drops in satisfaction
380. How can feedback be used to trigger re-training or model switching?
Aggregate low-score items into fine-tuning datasets
Identify prompts that need rerouting
Switch to different LLM or toolchain based on performance thresholds
381. How do you design an agent that can plan, retrieve, decide, and execute across tools?
Use LangGraph or FSM with memory
Define steps: Planning → Tool selection → Execution → Validation
Store intermediate states (scratchpad)
Chain with retry and reflection logic
382. What is the difference between reactive, proactive, and autonomous agents?
Reactive: Responds only when asked
Proactive: Initiates actions based on context
Autonomous: Plans and acts end-to-end with minimal input
383. How do you build guardrails around tool-using agents?
Define strict function schemas
Use sandboxed environments (e.g., Docker)
Implement output validators and budget caps
Add confirmation checkpoints for critical actions
384. What are signs that an autonomous agent is "looping" or stuck in reasoning?
Repeating same tool calls
Exceeding step limits
No new information retrieved
Stack trace or token loop alerts
385. What is a task decomposition agent and when should you use one?
Breaks complex goals into subtasks
Executes or delegates subtasks
Useful for long-form research, planning, coding agents
386. How can you assign confidence scores to agent actions?
Based on LLM self-evaluation
Tool output validation (e.g., success/failure)
Classifier models trained on agent-task success
Threshold confidence for escalation or retry
387. How would you evaluate autonomy vs. accuracy trade-offs?
Measure outcome quality vs. manual baseline
Log number of retries or errors
Time saved vs. correctness loss
Balance based on use case criticality
388. What strategies can prevent prompt escalation or tool abuse by agents?
Max prompt size/token limits
Rate-limit tool calls
Monitor tool usage logs
Auto-kill loops based on entropy or repeat patterns
389. How can autonomous agents collaborate on shared memory or context?
Use shared vector DB or JSON memory
Pass messages via protocol buffers or chat turns
Each agent appends to or reads from the memory state
Use coordination layers (e.g., AutoGen)
390. How do you manage cost predictability in long-running GenAI agent workflows?
Use token budget caps
Monitor per-task cost live
Batch non-critical subtasks
Route low-priority tasks to cheaper models (e.g., gpt-3.5-turbo)
391. What are the top blockers to GenAI adoption in traditional enterprises?
Data security and compliance fears
Lack of internal GenAI expertise
Resistance to change
Undefined ROI or unclear use case
392. How do you align GenAI strategy with business KPIs?
Map LLM features to time/cost savings, NPS, CSAT
Focus on automation in high-effort, low-creative tasks
Create KPIs like ticket reduction or content turnaround time
393. How do you work with legal teams on GenAI compliance reviews?
Share model cards, vendor policies
Document data handling flows (input/output retention)
Conduct DPIAs (Data Protection Impact Assessments)
Review licensing and usage policies
394. How do you address internal resistance to AI automation?
Start with augmentation, not replacement
Showcase success stories from peer orgs
Run internal pilots with champion teams
Offer training sessions and clear up misconceptions
395. What’s your playbook for rolling out a GenAI capability across multiple departments?
Identify top pain points per department
Run controlled pilot
Collect feedback → refine solution
Train local champions
Scale with shared platform + governance
396. How do you price GenAI-powered product tiers?
Usage-based (tokens, queries)
Feature-based (GenAI vs. non-GenAI plans)
Value-based (e.g., % cost saved or time reduced)
Offer sandbox tier to drive adoption
397. What are critical dependencies between GenAI features and data engineering teams?
Clean and labeled data
Embedding pipelines for RAG
Secure data access interfaces
API gateways for tool/function integration
398. How do you measure internal productivity lift from GenAI tooling?
Time-on-task before vs. after
Reduction in ticket backlog or documentation time
Self-service success rate
Surveys on perceived efficiency gains
399. What is your approach to evangelizing GenAI internally to non-technical stakeholders?
Live demos tailored to their workflow
Use ROI or time-saved metrics
Build champions in business units
Keep explanations simple and use analogies
400. How would you assess whether a GenAI prototype is ready for production rollout?
Meets accuracy thresholds
Has fallback and monitoring in place
Audited for safety and bias
Passes latency and cost budget checks
Receives positive user feedback in pilot
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