IVQA 551-600

Here's a structured and concise set of answers for Sections 56–60 of your GenAI interview prep:

✅ Section 56: Knowledge Grounding & Trustworthy Generation

  1. Ensure GenAI model grounds its output in source material Use Retrieval-Augmented Generation (RAG) pipelines, enforce contextual anchoring, and tune prompts to demand references. Use search → reason → respond frameworks with traceable links.

  2. Techniques for reducing hallucinations in RAG

    • Improve retriever recall and precision

    • Use chunk overlap for context continuity

    • Add filters (rerankers, confidence scoring)

    • Penalize hallucination during fine-tuning (RLHF)

  3. Tune prompt for citation or justification Add explicit instructions: “Cite your source. If not available, say 'source not found'.” Or use structured prompts like:

    Answer + Supporting Sentence + Source

  4. Zero-shot vs. Retrieval-Augmented Grounding

    • Zero-shot grounding: Model relies on internal knowledge; no retrieval.

    • RAG grounding: Real-time context from external sources fetched per query.

  5. Evaluate grounded vs. inferred answers Use:

    • Attribution scores

    • Answer-to-source overlap metrics

    • Human evals: “Is this claim traceable to any input?”

  6. Impact of chunk overlap Higher overlap improves contextual coherence but increases redundancy. Aim for 20–30% overlap for most tasks.

  7. Combine structured and unstructured knowledge Use hybrid RAG: SQL/graph responses + unstructured generation. Tools like LlamaIndex support knowledge fusion.

  8. Improve source attribution in multi-source summarization Use source-specific markers (e.g., source tags), embed IDs in context, and enable model to append (Source A) to claims.

  9. Audit for completeness and traceability

    • Log provenance chain: prompt → retrieved docs → output

    • Use Langfuse or Traceloop

    • Cross-check against coverage requirements

  10. Dynamically rerank or filter ungrounded outputs Apply re-ranking models post-generation (e.g., Cohere re-ranker), or LLM-as-judge to rate grounding fidelity.

✅ Section 57: Model Explainability & Transparency

  1. Use attention weights to understand behavior Visualize token-to-token dependencies, e.g., via BertViz. Note: attention ≠ explanation, just correlation.

  2. Saliency map for text Highlights which input tokens contributed most to output—can be derived from gradients or integrated gradients.

  3. Human-readable reasoning explanation Use prompt chaining: "Before answering, explain how you reached the conclusion." Or rationale-first prompting.

  4. Interpretable surrogate models Simplified models (e.g., decision trees, logistic regression) trained to mimic LLM outputs. Useful for debugging or compliance.

  5. Rationale generation Encourages transparency in reasoning. Enhances user trust and facilitates human-in-the-loop validation.

  6. Trace prompt → response → decision Log inputs/outputs with metadata. Use UUIDs for linking prompt, context, and output steps. Useful in regulated domains (e.g., finance, healthcare).

  7. Explainer modules in APIs Add an endpoint: /explain that provides token contributions, source references, and rationale. Use OpenTelemetry + Langfuse for logs.

  8. Tradeoff: explainability vs. creativity More explainability = more constrained outputs. For creative use cases (e.g., marketing), flexibility matters more than traceability.

  9. Identify attention collapse Watch for uniform attention patterns across tokens. Can indicate degraded performance in large models (especially in deep layers).

  10. Tools to visualize decisions

    • Captum (for PyTorch models)

    • LIT (Language Interpretability Tool)

    • Langfuse, Traceloop (for prompt-level tracing)

    • TransformerLens (for internal model inspection)

✅ Section 58: Global & Cross-Regional Trends

  1. Asian markets vs. Western adoption Asia focuses on super-app integration, education, and government control. Western markets lead in foundation models and startups.

  2. Data sovereignty in EU Requires on-premise or EU-region hosting, affects API-based GenAI solutions. Compliance with GDPR is critical (no OpenAI US-hosted endpoints).

  3. Multilingual LLM adoption in MENA/Africa

    • MENA: Arabic dialects pose tokenization/translation challenges

    • Africa: Low-resource language support via initiatives like Masakhane

  4. Emerging innovation clusters

    • UAE (Falcon), India (AI4Bharat), South Korea (Naver), Latin America (AI translation for indigenous languages)

  5. China regulations Require state approval, content filtering, alignment with political correctness. Limits model freedom but boosts domestic alternatives.

  6. Policy in developing nations Governments sponsor AI fellowships, training centers, and compute grants (India, Indonesia). Critical for talent pipelines.

  7. Censorship impact Limits access to training data, affects model diversity. Also alters model behaviors in constrained regions (e.g., ChatGPT in Iran).

  8. Localization challenges in South America

    • Slang, idioms, and indigenous language coverage

    • Lack of regional data

    • Payment infra for SaaS AI products

  9. Public-private LLM partnerships

    • UAE: Falcon via TII

    • India: Bhashini + Microsoft

    • France: Mistral backed by government grants

  10. Infrastructure disparities Cloud access, GPU affordability, and reliable internet affect fairness. Encourages on-device LLMs and regional hosting.

✅ Section 59: Security & Attack Surface Hardening

  1. Prevent prompt leakage Strip prior context, enforce content filters, rate-limit generative interfaces. Use token attribution for masking.

  2. Jailbreaking attack & prompt rewriting Jailbreaking bypasses safeguards. Use tree-of-thought verification, instruction templates, and model-level rejection sampling.

  3. Restrict to tool calls Use OpenAI function calling or LangChain tool routers to whitelist tools. Return error if undefined tool is invoked.

  4. Detect prompt injection Use regex-based guards, content classifiers, and context sanitization. Maintain separate system/user prompt tokens.

  5. Attack vectors in multi-agent setups

    • Input poisoning

    • Agent overreach

    • Message spoofing

    • Unauthorized tool access

  6. Runtime content filters

    • OpenAI moderation API

    • Anthropic safety classifiers

    • Custom keyword/regex pipelines with real-time audit

  7. AI firewalls / security gateways Tools like Prompt Armor, Lakera, and Protect AI that scan, filter, or rewrite prompts before LLM interaction.

  8. Least privilege for GenAI infra LLMs only access minimal tools, data, and endpoints needed for the task. Fine-grained auth + scoped credentials.

  9. Monitor for embedding inversion Apply differential privacy, monitor vector similarity leakage, and limit public embedding access.

  10. LLM sandboxing for SaaS Run models in isolated containers with request logging, usage throttling, and tenant-level keys to prevent spillover.

✅ Section 60: GenAI-Driven Business Transformation

  1. Identify augmentable areas Look for:

    • High-volume text tasks

    • Manual workflows (e.g., summarization, tagging)

    • Decisions based on unstructured data

  2. Cost reduction from GenAI

    • Fewer human agents (support)

    • Automated content ops

    • Faster document analysis (contracts, emails)

  3. GenAI Center of Excellence Cross-functional team with:

    • ML/Prompt engineers

    • Product managers

    • Legal, Risk, Compliance Responsible for tools, guardrails, and reusability.

  4. Change management

    • Executive alignment

    • Training + playbooks

    • Communication on job augmentation, not replacement

    • Pilot-first deployment

  5. Internal LLM for product managers

    • Ingests product data, feedback

    • Answers "What features are most requested?"

    • Suggests roadmap trade-offs based on usage patterns

  6. Quantify productivity gain Use KPIs:

    • Task completion time reduction

    • Cost per ticket

    • Quality score improvements Baseline before deployment is essential.

  7. GenAI pilot framework

    • Use case selection → Baseline → PoC → Evaluation → Rollout

    • Run it in a single team or function first (e.g., support, finance)

  8. Integrate with legacy ERP/CRM Use wrappers (REST/RPC), intermediate RAG agents, or LLM middleware that speaks both GenAI and system APIs.

  9. Feature vs. product strategy

    • Feature: Add GenAI to enhance a workflow (e.g., email summarization in CRM)

    • Product: GenAI is the product (e.g., AI-powered research assistant)

  10. Trust & compliance building

  • Transparent logs

  • Human-in-the-loop review

  • Ethics training

  • Clear escalation for unsafe outputs

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