@@ -8,103 +8,43 @@ const faqSchema = {
   mainEntity: [
     {
       '@type': 'Question',
-      name: 'How do you introduce yourself as an AI Technical Lead candidate?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I am an AI Technical Lead candidate with around 12 years of enterprise technology experience. My recent work focuses on AI engineering, GenAI solutions, RAG pipelines, and automation modernization. I have worked as a Technical Lead in AI/ML Engineering for a large healthcare enterprise, using LangChain, LangGraph, Azure OpenAI, Azure AI Search, embeddings, and multi-agent orchestration. One key project was a prior authorization AI solution where I owned the RAG pipeline, retrieval strategy, prompt structure, validation, and human review design.' },
+      name: 'What is asked in an AI Technical Lead interview?',
+      acceptedAnswer: { '@type': 'Answer', text: 'AI Technical Lead interviews usually cover RAG pipeline design, LLM monitoring, model selection, AI governance, production deployment, backend APIs, stakeholder handling, and domain-specific AI use cases like financial services compliance, fraud detection, credit scoring, KYC, and PCI.' },
     },
     {
       '@type': 'Question',
-      name: 'How do you walk through an end-to-end RAG pipeline?',
-      acceptedAnswer: { '@type': 'Answer', text: 'The pipeline starts with document ingestion. Azure Document Intelligence extracts text from PDFs. We clean text, remove noisy headers and OCR issues, then chunk the content. We generate embeddings using Azure OpenAI embedding models and store vectors and metadata in Azure AI Search. At runtime, the query is embedded and searched using hybrid retrieval (vector plus keyword). Retrieved chunks are passed to the LLM, which generates a structured response with source reference, confidence score, and decision reasoning. Low-confidence cases go to human review.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'What chunking strategy do you use in a RAG pipeline?',
-      acceptedAnswer: { '@type': 'Answer', text: 'We started with fixed-size chunking but it broke policy context in some cases. We moved to recursive chunking with overlap to keep related policy text together. We tuned chunk size based on document structure and added metadata to every chunk including business unit, document type, policy category, effective date, version, and source file.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How do you choose the LLM model for an enterprise RAG solution?',
-      acceptedAnswer: { '@type': 'Answer', text: 'We selected the model based on task complexity. For reasoning-heavy policy answers we used GPT-class models through Azure OpenAI. For embeddings we used Azure OpenAI embedding models. I compare models like GPT-5.5, Claude Opus 4.7, and Gemini based on accuracy, latency, cost, compliance, context length, tool support, and deployment availability. For factual enterprise answers, retrieval quality matters more than model choice alone.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How did you measure 85% hallucination reduction in RAG?',
-      acceptedAnswer: { '@type': 'Answer', text: 'We evaluated against a baseline dataset of historical business cases with domain-reviewer-validated expected outcomes. We ran the old pipeline and measured unsupported answers, then improved chunking, metadata, hybrid retrieval, prompt rules, and human review flow. We ran the same dataset again and compared before-and-after hallucination cases. The reduction came from fewer unsupported or policy-misaligned responses.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How does your healthcare AI experience translate to banking and financial services?',
-      acceptedAnswer: { '@type': 'Answer', text: 'The domain changes but the AI engineering pattern is similar. Healthcare has compliance, audit trails, policy-heavy decisions, sensitive data, and human review. Banking has the same: compliance, auditability, risk controls, policy documents, customer data, and governance. The production AI process is familiar: data quality, retrieval, validation, monitoring, and governance remain the same. I will need to learn banking terminology but not the engineering fundamentals.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How familiar are you with LLM governance, KYC, PCI, and compliance frameworks?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I have strong experience in regulated AI environments with HIPAA-style controls including audit logging, sensitive data protection, access control, human review, credential handling, and controlled LLM output. For KYC the workflow is identity validation, document verification, risk checks, exception handling, and audit trail. For PCI the focus is secure handling of cardholder data, encryption, least privilege, logging, and secure processing boundaries. The engineering mindset remains secure data handling, traceability, validation, and governance.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How would you reframe RAG architecture for transaction dispute processing?',
-      acceptedAnswer: { '@type': 'Answer', text: 'Transaction dispute RAG needs evidence plus policy retrieval. The system should retrieve invoice, PO, shipment proof, payment record, contract terms, emails, credit memo, and dispute policy. Structured data comes from ERP or SQL systems. Unstructured documents go through document extraction. Retrieval should use hybrid search with exact matching for invoice number, PO number, vendor ID, product code, and transaction date. The LLM generates a structured dispute summary with evidence, missing documents, policy rule, recommendation, confidence score, and source reference. Human review is required for conflicting or incomplete evidence.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How would you translate AI knowledge to fraud detection and credit scoring in banking?',
-      acceptedAnswer: { '@type': 'Answer', text: 'Fraud detection is a risk prediction and anomaly detection problem with inputs like transaction history, device signals, velocity patterns, user behavior, location, and historical fraud labels. Credit scoring is predictive risk modeling with inputs including income, repayment history, credit usage, delinquency, account age, and debt ratio. The AI lifecycle is the same: data preparation, feature engineering, model selection, evaluation, threshold tuning, deployment, monitoring, and feedback loop. For banking, explainability and governance are critical. I would start with supervised models, rules, anomaly signals, and human review for high-risk cases.' },
+      name: 'How do you explain a RAG pipeline in an interview?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Explain document ingestion, text extraction, chunking, embeddings, vector index, metadata filtering, hybrid search, LLM response generation, source citation, confidence score, logging, and human review for low-confidence cases.' },
     },
     {
       '@type': 'Question',
       name: 'How do you monitor a RAG pipeline in production?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I monitor three layers: system performance, retrieval quality, and LLM output quality. System metrics include latency, errors, timeouts, token usage, and dependency failures. Retrieval metrics include top-K quality, empty retrieval, metadata match, and wrong document retrieval. LLM metrics include hallucination rate, unsupported answer rate, schema failure, confidence score, and human override rate. Technical success is not enough — the answer must be grounded in correct retrieved context.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'What metrics do you track for RAG monitoring?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I track end-to-end latency, retrieval latency, LLM latency, token usage, API error rate, empty retrieval count, top-K relevance, metadata match rate, hallucination rate, unsupported answer rate, schema validation failure, low-confidence rate, human escalation rate, reviewer override rate, and cost per request. These metrics show both system health and AI quality.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'What alerting do you use for LLM production systems?',
-      acceptedAnswer: { '@type': 'Answer', text: 'We used Azure Monitor, Application Insights, Log Analytics, and custom application logs. Technical alerts covered API failures, high latency, timeout, and dependency failure. Functional alerts covered empty retrieval spike, schema validation failure, high low-confidence rate, and reviewer override spike. For AI systems, the API can be up but output quality can still degrade, so alerting must cover both infrastructure and AI quality.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How would you respond to a business asking for 100% accuracy in a banking chatbot?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I would not promise 100% accuracy. For banking that is risky. I would reframe the requirement: we can build a controlled, source-grounded, auditable chatbot that answers only from approved banking documents, cites source references, and refuses or escalates when approved context is missing. I would add RAG, metadata filters, prompt guardrails, confidence score, audit logging, and human escalation. The goal is safe, explainable, compliant answering — not free answering.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How do you decide between RAG and fine-tuning for an enterprise AI use case?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I choose based on the type of knowledge. If the knowledge changes often, I prefer RAG. Policies, pricing, compliance rules, and product documents change frequently and RAG keeps answers tied to current approved sources. Fine-tuning is better for behavior, format, classification style, or domain language. For enterprise use cases, RAG plus evaluation is usually safer first.' },
-    },
-    {
-      '@type': 'Question',
-      name: 'How do you handle prompt injection risk in a banking or compliance chatbot?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I separate system instructions, user query, and retrieved content. I add prompt rules to ignore user attempts to override system behavior. I only allow answers from approved retrieved sources. I add content filtering and policy checks. I log risky prompts and block requests asking for restricted data. I also test prompt injection examples before release. For banking, prompt injection is a security risk, not just an AI issue.' },
+      acceptedAnswer: { '@type': 'Answer', text: 'Track system metrics, retrieval quality, and LLM output quality. Important metrics include latency, token usage, API failures, empty retrieval, top-K relevance, hallucination rate, schema failures, low-confidence rate, human escalation, and reviewer override rate.' },
     },
     {
       '@type': 'Question',
-      name: 'How do you version prompts, embeddings, and indexed documents in production?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I version all three separately: prompt version, embedding model version, and document index version. Each response logs which prompt, model, and index version was used. When documents change, we re-index with version and effective date. Old documents are marked inactive if needed. This helps debugging — if output changes, we can trace whether it came from prompt change, document change, model change, or index change.' },
+      name: 'How do you evaluate hallucination reduction?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Use a ground truth dataset with reviewer-approved expected answers. Run the baseline pipeline, measure unsupported or wrong-policy answers, improve retrieval and prompts, rerun the same dataset, and compare before-and-after hallucination rate.' },
     },
     {
       '@type': 'Question',
-      name: 'How do you handle outdated documents in a RAG index?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I use metadata: effective date, expiry date, document version, status, and source system. During retrieval, filters prefer current active documents. Old documents should not be deleted blindly as they may be needed for audit or historical cases, but they should be marked inactive for current decisions. This is critical for banking and compliance use cases.' },
+      name: 'How do you handle banking AI compliance questions?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Focus on secure data handling, audit logging, explainability, source-grounded answers, access control, human-in-the-loop approval, KYC workflow understanding, PCI-safe data boundaries, and governance controls.' },
     },
     {
       '@type': 'Question',
-      name: 'How do you design human-in-the-loop approval for high-risk AI decisions?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I define clear thresholds: low confidence goes to human, missing source goes to human, conflicting documents go to human, high-risk transactions go to human. The reviewer sees AI summary, source evidence, confidence score, and recommendation. The reviewer can approve, reject, or correct. That feedback goes back into evaluation. HITL should not be an afterthought — it is part of production governance.' },
+      name: 'How do you decide between RAG and fine-tuning?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Use RAG when knowledge changes often, like policies, pricing, product rules, compliance documents, and banking procedures. Use fine-tuning for behavior, format, classification style, or domain language when the underlying knowledge is stable.' },
     },
     {
       '@type': 'Question',
-      name: 'How do you reduce token cost in production LLM applications?',
-      acceptedAnswer: { '@type': 'Answer', text: 'I reduce unnecessary context by tuning top-K, using better chunking, applying metadata filters, using smaller models for simple tasks, and larger models only for complex reasoning. Cache repeated answers where safe. Summarize long documents before final prompt when appropriate. Track token usage per request. Cost optimization should not reduce accuracy blindly.' },
+      name: 'How do you handle prompt injection in a banking chatbot?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Separate system instructions from user input, answer only from approved retrieved sources, block restricted requests, add content filtering, log risky prompts, validate output, and test prompt injection scenarios before release.' },
     },
     {
       '@type': 'Question',
-      name: 'What would you do in the first 90 days as an AI Hands-on Technical Lead?',
-      acceptedAnswer: { '@type': 'Answer', text: 'First 30 days: understand systems, data, use cases, current AI maturity, and business priorities. Next 30 days: identify high-value AI use cases and build or improve one controlled pilot. Last 30 days: define reusable architecture patterns including RAG foundation, model selection strategy, evaluation framework, monitoring dashboard, governance controls, and team delivery process. My goal is to create early trust, show measurable value, and build a foundation that can scale.' },
+      name: 'What should an AI Technical Lead do in the first 90 days?',
+      acceptedAnswer: { '@type': 'Answer', text: 'Understand systems, data, use cases, AI maturity, and business priorities. Then identify high-value use cases, improve one controlled pilot, define reusable RAG architecture, create evaluation framework, add monitoring, and establish governance controls.' },
     },
   ],
 };
 
@@ -6,19 +6,21 @@ <h2>Overview</h2>
 
 <p>The interview guide is based on a real AI Hands-on Technical Lead screening style focused on production RAG architecture, LLM monitoring, hallucination evaluation, financial services AI use cases, compliance, Java backend, Python APIs, and AI leadership scenarios.</p>
 
-<p>All company names, candidate names, and private identifiers are anonymized. Use these answers as short, technical, interview-ready responses.</p>
+<p>All company names, candidate names, and private identifiers are anonymized.</p>
+
+<p>Use these answers as short, technical, interview-ready responses.</p>
 
 <p><strong>Entity Summary</strong></p>
 <ul>
   <li><strong>Role:</strong> AI Hands-on Technical Lead</li>
   <li><strong>Skills:</strong> RAG, LLMs, Azure OpenAI, LangChain, LangGraph, Azure AI Search, Python APIs, Java backend, AI governance, HITL</li>
-  <li><strong>Domains:</strong> Healthcare AI, Financial Services AI, Banking AI, Pharma Enterprise AI, Compliance</li>
-  <li><strong>Use cases:</strong> RAG chatbot, fraud detection, credit scoring, KYC, PCI compliance, dispute processing, prior authorization AI</li>
+  <li><strong>Domains:</strong> Healthcare AI, Financial Services AI, Banking AI, Enterprise Compliance</li>
+  <li><strong>Use cases:</strong> RAG chatbot, fraud detection, credit scoring, KYC, PCI compliance, dispute processing, compliance policy search, document-grounded AI assistants</li>
   <li><strong>Interview type:</strong> Technical screening, client round, AI architecture review, production AI system design, leadership scenarios</li>
   <li><strong>May 2026 context:</strong> Azure OpenAI, GPT-5.5, Claude Opus 4.7, Gemini models, Microsoft Foundry model catalog, LangGraph, LlamaIndex, hybrid search, agentic AI, LLM monitoring, AI governance</li>
 </ul>
 
-<p><strong>Key topics covered:</strong> RAG pipeline design, chunking strategy, model selection, vector search index, production monitoring, hallucination evaluation, AI governance, banking AI translation, Java backend, Python Flask APIs, human-in-the-loop, first 90 days as AI lead, prompt injection defense, token cost optimization, conflict handling, fallback design, audit logging, KYC, PCI, fraud detection, credit scoring.</p>
+<p><strong>Key topics covered:</strong> RAG pipeline design, chunking strategy, model selection, vector search index, production monitoring, hallucination evaluation, AI governance, financial services AI translation, Java backend, Python Flask APIs, human-in-the-loop, first 90 days as AI lead, prompt injection defense, token cost optimization, conflict handling, fallback design, audit logging, KYC, PCI, fraud detection, and credit scoring.</p>
 
 <p><strong>Need real-time AI Technical Lead interview support?</strong> <a href="/proxy-interview-support/">Contact ProxyTechSupport</a> for live interview preparation and project support.</p>