How Modern AI Tools Are Really Built

A system design and cloud architecture perspective ## High-Level Architecture ## Why This Structure Works ## 1. AI Orchestrator as a Facade ## 2. Prompt Processing as a Pipeline ## 3. Strategy-Based Model Selection ## 4. Adapters for LLM Providers ## 5. Decorators for Safety and Optimization ## A Real Cloud AI Example ## Observability and Feedback ## Common Design Patterns in AI Platforms ## Final Thoughts ## Takeaway: ## Cloud AI systems are less about “calling an LLM” and more about building a resilient, observable, and evolvable backend around it AI tools like ChatGPT or Copilot often look magical from the outside. But once you step past the UI and demos, you realize something important: These systems are not magic — they are well-architected software platforms built on classic engineering principles. This post breaks down how modern AI tools are typically designed in production, from a backend and cloud architecture point of view. Most LLM-based platforms follow a structure similar to this: This design appears across different AI products, independent of cloud or model choice. The orchestrator acts as a single entry point while hiding complexity such as: Clients interact with a simple API without knowing how inference actually happens. Prompt handling is rarely a single step. It is typically a pipeline or chain of responsibility: Each step is isolated and easy to evolve. Different requests require different models: Using a strategy-based router allows runtime decisions without code changes. Production systems usually integrate multiple providers: Adapters keep the system vendor-agnostic. Cross-cutting concerns like: are typically implemented as decorators layered around inference logic. Consider an AI-powered support assistant running in the cloud: Behind the scenes, a lot more is happening asynchronously Inference does not end at the response: Observer and event-driven architectures allow AI systems to continuously improve. AI systems do not replace software engineering fundamentals. They depend on them. In real production platforms, the model is just one component. The real challenge is building a resilient, observable, and evolvable backend around it. Tags: #ai #systemdesign #cloud #architecture #backend #llm Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: Client (Web / Mobile / API) | v API Gateway | v AI Orchestrator (single entry point) | v Prompt Processing Pipeline - input validation - prompt templating - context / RAG | v Model Router (strategy based) | v LLM Provider (OpenAI / Azure / etc.) | v Post Processing - safety filters - formatting - caching | v Response Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Client (Web / Mobile / API) | v API Gateway | v AI Orchestrator (single entry point) | v Prompt Processing Pipeline - input validation - prompt templating - context / RAG | v Model Router (strategy based) | v LLM Provider (OpenAI / Azure / etc.) | v Post Processing - safety filters - formatting - caching | v Response CODE_BLOCK: Client (Web / Mobile / API) | v API Gateway | v AI Orchestrator (single entry point) | v Prompt Processing Pipeline - input validation - prompt templating - context / RAG | v Model Router (strategy based) | v LLM Provider (OpenAI / Azure / etc.) | v Post Processing - safety filters - formatting - caching | v Response COMMAND_BLOCK: User / App | v API Gateway (Auth, Rate limit) | v AI Service (Kubernetes) | +--> Prompt Builder | - templates | - user context | +--> RAG Layer | - Vector DB (embeddings) | - Document store | +--> Model Router | - cost vs quality | - fallback logic | +--> LLM Adapter | - Azure OpenAI | - OpenAI / Anthropic | +--> Guardrails | - PII masking | - policy checks | v Response Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: User / App | v API Gateway (Auth, Rate limit) | v AI Service (Kubernetes) | +--> Prompt Builder | - templates | - user context | +--> RAG Layer | - Vector DB (embeddings) | - Document store | +--> Model Router | - cost vs quality | - fallback logic | +--> LLM Adapter | - Azure OpenAI | - OpenAI / Anthropic | +--> Guardrails | - PII masking | - policy checks | v Response COMMAND_BLOCK: User / App | v API Gateway (Auth, Rate limit) | v AI Service (Kubernetes) | +--> Prompt Builder | - templates | - user context | +--> RAG Layer | - Vector DB (embeddings) | - Document store | +--> Model Router | - cost vs quality | - fallback logic | +--> LLM Adapter | - Azure OpenAI | - OpenAI / Anthropic | +--> Guardrails | - PII masking | - policy checks | v Response COMMAND_BLOCK: Inference Event | +--> Metrics (latency, tokens, cost) +--> Logs / Traces +--> User Feedback | v Event Bus (Kafka / PubSub) | +--> Alerts +--> Quality dashboards +--> Retraining pipeline Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: Inference Event | +--> Metrics (latency, tokens, cost) +--> Logs / Traces +--> User Feedback | v Event Bus (Kafka / PubSub) | +--> Alerts +--> Quality dashboards +--> Retraining pipeline COMMAND_BLOCK: Inference Event | +--> Metrics (latency, tokens, cost) +--> Logs / Traces +--> User Feedback | v Event Bus (Kafka / PubSub) | +--> Alerts +--> Quality dashboards +--> Retraining pipeline - retries and fallbacks - prompt preparation - safety checks - observability - validate input - enrich with context (RAG) - control token limits - format output - deep reasoning vs low latency - quality vs cost - fine-tuned vs general-purpose - OpenAI / Azure OpenAI - internal or fine-tuned models - PII masking - content filtering - rate limiting - Facade – simplify AI consumption - Pipeline / Chain – prompt flow - Strategy – model routing - Adapter – provider integration - Decorator – safety and optimization - Observer / Pub-Sub – monitoring and feedback - CQRS – inference isolated from training