Working through this module?0 of 19 done
Module 16 · Page 17 of 20 · 8 sections

Real-world Case Studies

Four worked cases, a RAG support chatbot, e-commerce recommendations, document intelligence and fraud detection, each from problem to architecture.

07RAG System Design14Evaluation and Testing17End-to-End Project Design
16.1

1. Introduction

  • Real-world case studies help us understand how AI systems are designed, deployed and used to solve actual business problems.
  • They cover the end-to-end journey from problem statement to system design, implementation and impact.
  • This topic is important for interviews and helps in learning practical design and trade-offs.
16.2

2. Why Case Studies are Important?

  1. 1Understand real-world problem solving approach.
  2. 2Learn system design trade-offs.
  3. 3See how theoretical concepts are applied in practice.
  4. 4Help in interview discussions (design, scalability, cost).
  5. 5Learn from success stories and challenges.
  6. 6Improve business and product thinking.
  7. 7Understand data, model and infrastructure choices.
  8. 8Get ideas for your own projects.
16.3

3. Types of Real-world AI Applications

1
Chatbots & Virtual Assistants
Customer support.
2
Recommendation Systems
E-commerce, OTT.
3
Document Understanding
Finance, healthcare.
4
Search & Knowledge Retrieval
RAG.
5
Image & Video Analysis
Security, healthcare.
6
Fraud Detection
Banking, payments.
7
Predictive Analytics
Demand, churn, risk.
8
AI Agents
Automation, multi-step tasks.
16.4

4. Case Study 1: Chatbot with RAG (Customer Support)

Problem statement. A company wants to build a chatbot that can answer user queries using internal documentation (PDFs, help center, manuals).

User Query
Query EmbeddingSentenceTransformer
Vector DatabaseQdrant
Relevant ContextRetrieval
LLMOpenAI / Llama
Final Response to User

Key points

  1. 1Use a vector database for document retrieval.
  2. 2Use chunking, embeddings and similarity search.
  3. 3Add a prompt with retrieved context to the LLM.
  4. 4Implement caching for frequent queries.
  5. 5Track metrics (response time, relevance, user feedback).
Related07RAG System Design
16.5

5. Case Study 2: Recommendation System (E-commerce)

Problem statement. An e-commerce platform wants to recommend products to users based on their search history, purchases and browsing behavior.

User & Product DataClick, purchase
Data ProcessingETL
Feature Engineering
Model TrainingCollaborative filtering / deep learning
Top-N Recommendations

Key points

  1. 1Use content-based and collaborative filtering.
  2. 2Handle the cold start problem for new users.
  3. 3Use online and offline evaluation metrics (Precision@K, Recall@K).
  4. 4Continuously retrain the model with new data.
  5. 5Implement real-time recommendation using caching and a feature store.
16.6

6. Case Study 3: Document Intelligence (Finance / Banking)

Problem statement. A bank wants to extract key information (name, account number, amount, date, etc.) from various documents (KYC, invoices, statements).

DocumentPDF / image
PreprocessingOCR
Text Extraction
Information ExtractionNER / LLM
Structured DataJSON

Key points

  1. 1Use OCR for scanned documents (Tesseract / Textract).
  2. 2Use LLM or NER models for field extraction.
  3. 3Handle different document formats and layouts.
  4. 4Validate extracted data with business rules.
  5. 5Use human-in-the-loop for low confidence results.
16.7

7. Case Study 4: Fraud Detection (Payments)

Problem statement. A payment platform wants to detect fraudulent transactions in real time to prevent financial loss.

Transaction DataReal-time
Feature EngineeringRules + ML
Fraud Detection Model
Risk ScoreReal-time
ActionAllow / block / review

Key points

  1. 1Use supervised and unsupervised learning techniques.
  2. 2Handle imbalanced data (fraud cases are rare).
  3. 3Use real-time streaming (Kafka + Flink / Spark).
  4. 4Set a threshold based on risk appetite.
  5. 5Continuously monitor and retrain with new fraud patterns.
16.8

8. Other Real-world Examples

1
Netflix
Personalized movie recommendations.
2
Uber
ETA prediction and demand forecasting.
3
YouTube
Video recommendations and content moderation.
4
Healthcare
Medical image analysis (X-ray, MRI).
5
HR Tech
Resume screening using LLM.
6
Enterprise
Internal knowledge search using RAG.
7
Customer Support
AI agents for automated ticket handling.
8
Autonomous Vehicles
Real-time computer vision and decision making.