AI System Architecture Concepts

Welcome to the definitive guide on modern AI system architecture. Building intelligent applications today goes far beyond simple API calls to a language model. To create robust, scalable, and secure AI systems, developers need to master a specific set of architectural patterns and tools.

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Overview of Key Concepts

Here is a high-level look at the nine foundational concepts you need to know:

#	Concept	One-liner
1	Vector Database	Stores embeddings as vectors for semantic search and retrieval
2	Embedding Model	Converts text into high-dimensional vectors capturing semantic meaning
3	RAG	Retrieves relevant context from a knowledge base to augment LLM responses
4	Prompt Engineering	Designs effective prompts to guide LLMs for accurate and relevant responses
5	Semantic Cache	Caches similar queries and responses using semantic similarity to reduce costs
6	MCP	Standard protocol for connecting models with external data, tools, and services
7	AI Agent	Autonomous system that perceives, plans, and acts to achieve goals
8	Function Calling	LLM invokes external functions/APIs to get real-time data or take actions
9	Agent Guardrails	Enforces safety, policies, and quality checks to ensure reliable AI outputs

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How These Concepts Work Together

These nine concepts aren't isolated — they form a layered, interconnected AI system:

┌──────────────────────────────────────────────────────────────────┐
│                        USER / APPLICATION                        │
└──────────────────────────┬───────────────────────────────────────┘
                           │
                    ┌──────▼──────┐
                    │  AI AGENT   │  ← orchestrates everything
                    └──────┬──────┘
          ┌────────────────┼────────────────┐
          │                │                │
   ┌──────▼──────┐  ┌──────▼──────┐  ┌──────▼──────┐
   │  Function   │  │     RAG     │  │   Prompt    │
   │  Calling    │  │  Pipeline   │  │ Engineering │
   └──────┬──────┘  └──────┬──────┘  └─────────────┘
          │                │
   ┌──────▼──────┐  ┌──────▼──────┐  ┌─────────────┐
   │  External   │  │  Embedding  │  │  Semantic   │
   │   APIs      │  │   Model     │  │   Cache     │
   └─────────────┘  └──────┬──────┘  └─────────────┘
                           │
                    ┌──────▼──────┐
                    │   Vector    │
                    │  Database   │
                    └─────────────┘

All wrapped by:   Agent Guardrails + MCP (tool connectivity)

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Sub-Pages

Dive into each concept in detail:

1. 📦 Vector Database — How to store and search semantic data
2. 🔢 Embedding Model — Converting meaning into numbers
3. 🔍 RAG (Retrieval Augmented Generation) — Grounding LLMs in your data
4. ✍️ Prompt Engineering — Communicating effectively with LLMs
5. ⚡ Semantic Cache — Speed and cost optimisation
6. 🔌 MCP (Model Context Protocol) — Universal tool connectivity
7. 🤖 AI Agent — Autonomous, goal-driven AI systems
8. 📞 Function Calling — LLMs that interact with the world
9. 🛡️ Agent Guardrails — Safety, security, and policy enforcement