Capstone: One Complete Use Case (All Concepts Together)

Use case: Build an AI Support Agent for an e-commerce company that answers customer questions, checks order status, and creates return requests safely.

1) Problem statement

Customers ask: "Where is my order?", "Can I return this item?", "What is refund policy?" The company wants fast answers, lower support load, and safe automation.

2) Full architecture (combined concepts)

flowchart LR U[Customer question] --> P[Prompt layer] P --> L[LLM] P --> R[Retriever] R --> V[Vector DB with policy docs] V --> R R --> L L --> A[Agent planner] A --> T1[Tool: Order API] A --> T2[Tool: Returns API] A --> G[Guardrails] G --> H{High risk action?} H -- Yes --> M[Human approval] H -- No --> O[Execute action] M --> O O --> X[Final answer + action status] X --> Obs[Logs, metrics, traces]

3) Where each concept fits

Concept	How used in this use case
LLM basics	Generates natural language replies to users
Tokens/context	Controls prompt size, chat history, and doc chunks
Prompt engineering	System prompt defines support tone, policy, and output format
Embeddings	Policy documents converted to vectors for semantic retrieval
RAG	Retrieves latest return/refund rules before answer generation
Agentic behavior	Plans steps and calls tools (order lookup, return creation)
Tool calling	Uses structured function calls with validated parameters
Memory	Short-term: current chat; long-term: user preferences/history
Reflection loop	Checks if output is policy-compliant and complete
Guardrails	Blocks unsafe requests and sensitive data leaks
HITL	Manager approval for high-value refunds
Observability	Tracks accuracy, latency, tool failures, and cost

4) End-to-end execution example

User: "I received a damaged product. Can you return it and tell me refund time?"

Agent understands intent: return + policy question.
RAG retrieves "damaged item return policy" and "refund SLA."
Agent calls Order API to verify order and delivery date.
Agent calls Returns API to create return request.
Agent validates response and generates final answer with ticket ID.
System logs all steps for monitoring and audit.

5) Evaluation checklist for this use case

Accuracy: answer matches policy and order data.
Latency: response within target (for example, less than 3 seconds).
Cost: token usage and tool calls within budget.
Safety: no policy violations, no sensitive leakage.
Reliability: handles API timeout with retry/fallback.

6) Why this capstone matters

This one workflow demonstrates how Gen AI + Agentic AI are combined in real products: model reasoning, retrieval, actions, safety controls, and production monitoring.