-- if element is repeated , then ans will be wrong so make use of distnce on stream 1st ,then sort it as below to get correct output if...

1. Chunk by Semantic Boundaries (NOT fixed size only) Split by sections, headings, paragraphs , or logical units. Avoid cutting a sentence or concept in half. Works best with docs, tech specs, policies, manuals. Why:  Models retrieve more accurate and meaningful context. 2. Use Hybrid Chunking (Semantic + Token Length) First split semantically. THEN enforce a max token limit (e.g., 300–500 tokens). Drop chunks that are too small (e.g., <50 tokens). Ideal size: ➡️ 300–500 toke

USE CASE: LARGE RETAIL BANK – DIGITAL CHANNEL MODERNIZATION 🔹 Business Context A large retail bank wants to “modernize” its digital channels (internet banking + mobile apps). Constraints: Heavy regulatory compliance (RBI, PCI) Legacy core banking system (cannot change easily) Multiple future initiatives planned over 3–5 years: New mobile app Open banking APIs Partner integrations (fintechs) AI-based personalization (future) They explicitly say: “We don’t want to keep changin

1️⃣ Zero-Shot Prompt What it is: No examples, just instructions. When to use: Simple, well-defined tasks Fast responses When model already “knows” the domain Example: “Summarize this loan agreement in 5 bullet points.” Enterprise use: Email drafting Basic summaries Simple Q&A bots ⚠️ Risk:  Output can vary Not reliable for compliance-critical use 2️⃣ One-Shot Prompt What it is: Instruction + 1 example When to use: When output format matters Light guidance improves accuracy Ex

Build vs Buy vs Partner – VP Digital Architecture View How I Frame the Decision “Build vs Buy vs Partner is a portfolio decision, not a single-system decision.As VP Digital Architecture, my responsibility is to balance speed, control, risk, and long-term sustainability—while keeping regulators comfortable.” The Scenario Program:  Digital Lending & Client Onboarding Modernization Enterprise Type:  Tier-1 Bank / Large NBFC Timeline:  18–24 months Constraints:  Regulatory compl

Digital Lending – Regulatory Coverage (rbi Aligned) Digital Lending Application – Regulatory Compliance Coverage (India / RBI) This document maps Regulatory Requirement → Platform Capability → Microservices / Controls → KPIs & Audit Evidence . 1. RBI Digital Lending Guidelines (2022–2023) Regulatory Expectations Disbursement & repayment only between borrower and Regulated Entity (RE) Transparent disclosure of APR, fees, tenure Explicit borrower consent No hidden charges or mi

VP Technology / Architecture – Strategic Framework 1️⃣ Strategic (12–36 months) Focus: Long-term business and technology transformation Objectives / Goals: Cloud-First & API-First:  Modernize platforms, enable scalability, and foster integration ecosystems AI-First:  Drive intelligent automation, predictive analytics, and customer-centric AI capabilities Risk-First / Zero Trust:  Embed security and risk mitigation in all strategic decisions Regulatory Compliance & Governance:

is citation & grounded context is same?? ===== Good question — they’re related but not the same , and interviewers often use them loosely, which causes confusion. Short answer ❌ Citation ≠ Grounded Context ✅ Citation supports grounding , but grounding is broader. 1. What is Grounded Context ? Grounded context  means the AI response is anchored to trusted, known data , not hallucinated. The model is constrained to: Enterprise documents Policies Contracts Databases Knowledge ba

🏦 KEY BANKING RISK METRICS (EXPLAINED SIMPLY) 🔍 What is AUC  (in Credit / Risk Models)? AUC = Area Under the ROC Curve In simple terms: AUC measures how well a model can distinguish between good and bad customers (non-default vs default). 🎯 Intuitive Meaning (Very Important) AUC = 0.5  → Model is no better than random guessing AUC = 1.0  → Perfect separation (never happens in real life) Typical BFSI Ranges AUC Range Interpretation 0.50–0.60 Poor 0.60–0.70 Weak 0.70–0.75 Ac

✅ How to Build Estimation for a GenAI Use Case Step 1: Identify the Use Case Scope What business problem are you solving? (Customer support, document processing, fraud detection, etc.) Who are the end users? (Internal ops, customers, auditors) What is the expected volume? (e.g., 1M queries/month) What data sources are involved? (Structured, unstructured, third-party) Example: Use case: AI copilot for customer support Volume: 50,000 tickets/month Channels: Web, mobile, call ce

🧠 What is Model Tiering  in GenAI? Model tiering  is an architectural strategy  where multiple AI models of different sizes, costs, and capabilities  are used together, and each request is routed to the most cost-effective model that can meet the requirement . Not every query needs the most powerful (and expensive) model. 🎯 Why Model Tiering is Critical (Especially in BFSI) Without tiering: Every request hits a large LLM Costs explode Latency increases Risk surface grows Wi

1️⃣ Model Performance & Business Accuracy Risk AI accuracy not translating to business value What You Did Model governance, A/B testing, human-in-loop Continuous retraining pipelines Metrics Credit / risk model accuracy: +5–10% uplift Fraud false positives: ↓ 20–30% 2️⃣ Cost Control & AI Economics (Critical for GenAI) Risk Uncontrolled inference cost What You Did Model tiering (small vs large LLMs) Semantic caching & prompt optimization Metrics Inference cost reduced by 30–50

Multi-tower enterprise solutions  refer to large, end-to-end business solutions that span multiple technology and delivery “towers” , each owned by different teams, vendors, or competencies, but orchestrated as one integrated outcome . What does “tower” mean? A tower  is a major capability or service domain in enterprise IT. Typical enterprise towers include: Technology Towers Application Development & Maintenance (ADM) Cloud & Infrastructure  (AWS / Azure / GCP, DC, Network)

⭐ “A transformation program is running for 6 months. Business says it is not delivering the value they expected. What will you do?” “When business says a 6-month transformation isn’t delivering value, I immediately move into a structured recovery mode — understand, validate, re-align, and re-execute.” 🔶 1. Run a Rapid “Value Diagnostics” Assessment (1–2 weeks) I do a fast, structured diagnostic  to identify where the breakdown is: a. Business Value Gap Are we building the ri

⭐ EA Strategy in Case of a Merger (M&A) My EA strategy for a merger focuses on four pillars: discover, decide, integrate, and optimize.The goal is business continuity + synergy + tech consolidation. ✅ 1. Rapid Discovery & Baseline Assessment (Day 0–30) The first step is visibility . a. Current Enterprise Landscape Assessment Application inventory Integration landscape Data flows and critical master data Tech stack, infra, cloud usage Security posture, controls, and vulnerabil

We will take a one walk through where cusotmer will use copilot to query info on structural data set like "what is my credit score??". then ollama model will respond in NLP way. Objective ----- 👉 This is a complete, compilable, enterprise-style Spring Boot GenAI POC  with: Prompt stored as code in Git Prompt registry Fake microservice API Prompt injection Local Ollama call Swagger UI testing Model + prompt version returned in API Assumption: 1.We have contextual API that wil

What the Knowledge Hub is and goals High-level architecture (components) Ingestion pipeline (sources → chunking → embeddings) Indexing & storage (vector DB + metadata store + KG) Retrieval / RAG integration & runtime APIs Governance, QA, and audit controls Security, privacy & compliance (BFSI focus) Operations, monitoring & SLOs Example schemas / payloads and prompt template hints Interview-ready one-liners and FAQs 1 — What is the Knowledge Hub (Goals) A Knowledge Hub is the

1️⃣ What is RAG (Retrieval Augmented Generation)? RAG = Retrieval + LLM Reasoning Instead of relying only on what the LLM was trained on, we: Retrieve  relevant enterprise-approved documents (policies, procedures, contracts, past cases) Augment  the LLM prompt with this retrieved content Let the LLM generate  a grounded, evidence-based answer Conceptual Flow User Question ↓ Retriever (Vector / Search) ↓ Relevant Chunks from Enterprise Knowledge ↓ Prompt = Question + Retrieved

Unified KPI Table — Before vs After Modernization (Strategic + Tactical + Operational) KPI Category KPI Before Modernization After Modernization Strategic (Business Outcomes) Customer Onboarding TAT 3–5 days < 30 minutes Strategic (Business) Digital Lending Approval Time 24–48 hours 10–15 minutes Strategic (Business) Self-Service Digital Adoption 30% 75%+ Strategic (Business) Net Promoter Score (NPS) 45 70+ Strategic (Business) Customer Retention 82% 93% Strategic (Financial)

✅ 1. Banking Capability Map → KPI Mapping (Strategic + Tactical + Operational + Delivery + Governance) A. BANKING CAPABILITY MAP (TOP-LEVEL) These are typical enterprise-wide business + technical capabilities: 1. Customer & Channel Capabilities Omni-channel onboarding Self-service portals Mobile banking Branch office servicing Assisted journeys (RM/agent) API channels (external partners, DSA) 2. Lending Capabilities Digital onboarding KYC/AML/Sanctions screening Risk scoring