IVQA 301-350
301. How do GenAI models handle multilingual inputs?
Multilingual models (e.g., mT5, XGLM) are trained on multi-language corpora
LLMs like GPT-4 and Gemini detect language automatically
Tokenizers use shared subword units across languages
302. What are alignment challenges when generating content in multiple languages?
Cultural nuance and tone mismatch
Idioms and metaphors don’t translate cleanly
Inconsistent safety filters across languages
Bias may be more pronounced in underrepresented languages
303. How do you evaluate translation accuracy in low-resource languages?
Use BLEU, chrF, or COMET scores
Human review for fluency and adequacy
Leverage synthetic round-trip translation (A → B → A)
Compare to parallel corpora (if available)
304. How would you fine-tune a model to perform better on a specific regional dialect?
Curate dialect-specific corpora (e.g., social media, subtitles)
Fine-tune using LoRA or PEFT
Adjust tokenizer if needed for unique spellings
Validate on regional benchmarks or human evals
305. What are the best multilingual open-source LLMs available today?
mBART, mT5, XGLM (Meta), ByT5
Mistral multilingual variants, Gemma, BLOOMZ-mt
Some versions of LLaMA 3 and OpenHermes support >20 languages
306. How do you ensure cultural sensitivity in multilingual GenAI output?
Use culturally diverse training data
Apply post-generation filters or classifiers for stereotypes or bias
Involve native reviewers in evaluation
Avoid direct translation of culturally loaded terms
307. How do embeddings behave across languages? Are they comparable?
In multilingual models, embeddings map similar concepts across languages to nearby vectors
Can be used for cross-lingual retrieval
Alignment quality depends on language pair and training data
308. What are strategies to prevent code-mixing errors in multilingual chatbots?
Detect user language per session or utterance
Lock model responses to the detected language
Train/fine-tune on language-consistent dialogs
Penalize unwanted language switches during decoding
309. How do GenAI models handle right-to-left (RTL) scripts like Arabic or Hebrew?
Handled at the tokenizer/rendering level
Model itself is language-agnostic but UI must support RTL rendering
Preprocessing must preserve word order and punctuation in RTL
310. What are multilingual benchmarks like XNLI or XTREME, and why do they matter?
XNLI: Natural language inference in 15+ languages
XTREME: Covers tasks like QA, NLI, NER, and retrieval across 40+ languages
Essential for evaluating cross-lingual transfer and fairness
311. How would you architect a GenAI system that automatically fills forms using PDFs?
Extract text using OCR/PDF parsers
Use LLM to map fields from extracted text to form schema
Auto-fill using form templates (PDF, HTML, JSON)
Apply validation before submission
312. How can GenAI be used to control CLI or OS-level tools safely?
Use LLM to generate structured commands
Pass commands to a sandboxed executor (e.g., Docker, subprocess with ACL)
Restrict allowed commands or arguments
Log and audit all executions
313. What’s the difference between Zapier MCP and OpenAI function calling?
Use Case
External workflows, automation
Internal tool integration
Auth
Built-in OAuth & triggers
Manual function routing
Interface
No-code
Code-first (JSON schema based)
314. How do you trigger real-world automation using GenAI?
Use LLM to parse intent and generate API call or command
Connect via function calling or LangChain Tools
Trigger workflows (e.g., Twilio for SMS, SendGrid for email)
Validate intent before execution
315. How do you use Selenium or Playwright with GenAI for browser control?
LLM generates action plans (e.g., “click login button, fill form”)
Pass to Selenium/Playwright script with selector matching
Confirm state via screenshots or DOM parsing
Useful for testing, scraping, RPA
316. How can you use GenAI for test automation in software QA?
Generate test cases from user stories or code
Convert bug reports to reproducible steps
Create input permutations for fuzz testing
Summarize test results or logs
317. How do you integrate GenAI with calendar tools for smart scheduling?
Parse intent ("Book meeting next week with team")
Call APIs like Google Calendar or Outlook
Retrieve availability and generate invite
Allow confirmation before booking
318. How do you ensure safe tool use in LLM-powered autonomous agents?
Add tool schemas with strict type checking
Limit execution scope (time, resources, APIs)
Use audit logging and sandboxed environments
Implement retry and validation logic
319. What are common tool abstractions in LangChain or AutoGen?
Tool: Wrapper for external function with name/description
AgentExecutor: Orchestrates tool selection and calling
AutoGen Agent: Structured role + tool + message protocol
Tools include search, calculator, DB query, file system
320. How do you manage API credentials securely in GenAI workflows?
Store in secret managers (e.g., Vault, AWS Secrets, env vars)
Never hard-code keys in prompts or logs
Limit token scopes and rotate periodically
Use role-based access control (RBAC)
321. How do you write unit tests for GenAI prompt outputs?
Define input → expected output pairs
Use fuzzy matching (e.g., semantic similarity)
Validate structure (e.g., JSON schemas)
Run tests across model versions
322. What is prompt fuzzing and why is it important?
Automated generation of varied prompts to test model robustness
Helps uncover edge-case behavior, prompt injection vulnerabilities
Can test tone, formatting, adversarial variants
323. How do you conduct regression testing for GenAI behavior?
Store golden prompts + expected outputs
Compare new model outputs to historical ones
Use metrics like BLEU, cosine similarity, or human review
Run as part of CI pipeline
324. How can you simulate adversarial attacks during model testing?
Inject jailbreak prompts, misleading instructions
Use security-focused fuzzing libraries
Monitor for compliance violations or safety lapses
325. What are gold-standard responses in GenAI evaluation?
Human-authored or curated outputs deemed correct
Used as ground truth for BLEU, ROUGE, or accuracy
Essential for supervised fine-tuning or eval benchmarks
326. How do you evaluate hallucination vs. paraphrasing vs. error?
Hallucination: Fabricated fact not in context
Paraphrasing: Different wording, same meaning
Error: Wrong or misleading content
Use human annotation or fact-checking tools
327. What’s the best way to do continuous integration for GenAI prompts?
Version control prompts (Git)
Include eval suite with semantic and structural tests
Validate before merge or deploy
Use prompt templating systems (e.g., Jinja, DSPy)
328. What metrics would you track in a GenAI system post-release?
Token cost per user/task
Task completion rate
Satisfaction scores (thumbs up/down)
Latency, fallback rate, hallucination frequency
329. How do you identify slow degradation of GenAI model quality?
Monitor drift in user feedback or embeddings
Compare monthly performance benchmarks
Use canary models for A/B checks
Analyze token usage vs. success rate over time
330. How do you validate performance on rare edge cases?
Curate datasets for low-frequency scenarios
Use counterfactuals (e.g., “What if X didn’t happen?”)
Include diversity tests (language, domain, input styles)
Apply stress testing frameworks
331. How do LLM agents decide when to use tools vs. generate answers?
Use internal scoring or conditional prompts
Based on presence of keywords (e.g., "calculate", "search")
Maintain tool metadata (use cases, triggers)
Some use planner modules to evaluate options
332. How do you enable recursive self-reflection in agents?
Add a “reflection” step post-output
Ask the model: “Was this answer accurate? How can it improve?”
Use critic agents or inner monologues (e.g., AutoGPT, ReAct)
Append feedback loop into scratchpad
333. What’s the difference between plan-and-execute vs. chain-of-thought approaches?
Structure
Creates full plan first
Step-by-step reasoning
Flexibility
Less adaptive mid-task
Dynamically adjusts per step
Example
AutoGPT, LangGraph
ReAct, PAL
334. How would you implement a research agent that performs web searches and writes a report?
Tools: search API, summarizer, notepad, report generator
Plan: Define subtopics → search → extract facts → synthesize
Use agent loop with memory and task tracker
Output final report in Markdown or PDF
335. How do you tune an agent’s decision-making loop for complex task completion?
Define stopping conditions and success criteria
Add scoring for each tool/action step
Track failures and retries
Use RL or feedback-based improvement
336. What is dynamic memory injection in agent design?
Retrieve relevant memory during runtime (e.g., via embeddings)
Inject into prompt before generation
Reduces context window overload
Supports long-horizon reasoning
337. How do you use graph structures for tracking agent plans?
Nodes = subtasks or decisions
Edges = dependencies or transitions
Visualize with LangGraph or custom FSMs
Enables retry, rollback, or parallel planning
338. What is inter-agent communication and when is it useful?
Agents exchange messages (e.g., UserAgent → CriticAgent)
Useful for role specialization and consensus building
Enables collaborative workflows and task sharing
339. How would you simulate user feedback in agent training?
Use synthetic feedback (thumbs up/down based on rules)
Log real-world usage and inject as rewards
Pre-train critic model to generate feedback
Fine-tune with RLHF or scoring loops
340. How can agents collaborate to summarize, critique, and improve a document?
One agent summarizes
Second critiques based on rules (clarity, tone)
Third suggests improvements
Final agent synthesizes into final output
341. How do GenAI models handle time-based logic or sequences of events?
Trained on temporal patterns in language
Struggle with date math, implicit timelines
Use prompts like “first, next, finally” to guide logic
342. How would you get an LLM to generate accurate timelines?
Provide structured input (event + date)
Ask to order or format as timeline
Use tools like date parsers for verification
Prompt with examples for consistent output
343. What’s the difference between temporal inference and causal reasoning?
Temporal: "Event A happened before B"
Causal: "Event A caused B"
Causal reasoning requires external knowledge or strong world models
344. How do you evaluate event consistency in long-context generation?
Track entities and timestamps across paragraphs
Use logical consistency checks (“Did event B occur after A?”)
Apply temporal QA tools or symbolic validators
345. How do LLMs fail at reasoning about calendars or durations, and how do you fix it?
Confuse relative dates ("next Monday")
Miscalculate durations
Fix: use date libraries, tool calls, or fine-tune on calendar tasks
346. How would you simulate memory evolution over time for an agent?
Store time-stamped entries
Decay or prioritize memory slots
Inject memory state relevant to current prompt
Use context-aware memory retrieval
347. How can GenAI track state changes in a long conversation?
Log state as key-value store
Update state after each turn (e.g., shopping cart, task progress)
Summarize past states periodically
Reinject into prompt as needed
348. How do you implement reminder and follow-up functionality in a GenAI assistant?
Parse intent + time from user input
Store in task scheduler (e.g., cron, APScheduler)
Trigger prompt generation when time is reached
Confirm with user before action
349. What are ways to encode temporal facts into retrieval systems?
Store timestamps with document chunks
Add temporal filters in vector DB queries
Use time embeddings
Combine symbolic and vector search
350. How would you compare two GenAI outputs for narrative coherence over time?
Check event order consistency
Use temporal logic rules (e.g., before/after violations)
Human review or automated QA
Use scoring models trained on narrative structure
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