AI Application Architecture
Architecture for production AI applications with model serving, RAG pipelines, evaluation, and cost controls that survive contact with real users.
Components
Considerations
Alternatives
Complexity
Fit
When this blueprint fits
And when to walk away from it
When to use this
You are shipping LLM-powered features beyond a demo: a copilot, an assistant, a generation pipeline, or a workflow with model calls in the critical path. The right starting point when users will tolerate weirdness once but not twice.
When NOT to use this
If you only need a single feature with no streaming, no tools, and no evaluation needs, a direct provider SDK call is enough. Defer this architecture until you have at least three model call sites or one production-critical flow.
Architecture
System components
Key building blocks of this architecture, layered from infrastructure up.
LLM Gateway
RAG Pipeline
Prompt Management
Evaluation System
Cost Management
Safety and Moderation
Observability and Tracing
Planning
Critical considerations
The things I have learned the hard way and would not skip on the next build.
Options
Alternative approaches
Where I would consider a different shape entirely, with the trade-offs spelled out.
Implementation
Related playbooks
Step-by-step guides for the harder parts of this architecture.
Building RAG Applications
Retrieval-augmented generation looks simple in a demo and stays simple until your knowledge base is bigger than a thousand documents, chunks overlap badly, or relevance scores stop making sense. This is my end-to-end RAG playbook: document processing, embedding pipelines, retrieval tuning, prompt design, and the evaluation harness that tells you whether changes are actually improving results.
Shipping AI Features Without the Hype Tax
Most AI features ship as a demo that survives one round of investor questions and then quietly dies in production. This is the discipline that gets AI features past that wall: small scope, real evals, careful rollouts, and instrumentation that catches drift early. The same loop I run when I add AI capabilities to an existing product, on a real timeline with real users.
In practice
Related case studies
Where I have applied this blueprint to real builds and what changed in practice.
AI Document Processing Platform
An AI-powered document processing system that transformed how a legal team handled contract review, due diligence, and compliance.
AI-Powered Enterprise Search
An AI-powered search platform that unifies search across dozens of enterprise systems with natural-language understanding and contextual results.
Thinking
Related insights
Essays where I argue the trade-offs behind the choices in this blueprint.
Building Production RAG Systems
RAG looks simple in demos but is notoriously hard in production. Here's a comprehensive guide to building RAG systems that actually work, based on real deployment experience.
An LLM Evaluation Framework That Works
How to systematically evaluate LLM applications with a practical framework covering automated metrics, human evaluation, and continuous monitoring.
Prompt Engineering for Production
Production prompts need to be reliable, testable, and maintainable. Here's how to treat prompts as code with proper engineering practices.
Need help implementing this blueprint?
I help teams adapt blueprints like this to their specific requirements and ship from planning through production.
AI Systems
More in this category
Other blueprints with overlapping concerns.
RAG Application Blueprint
Reference architecture for retrieval-augmented generation apps covering embedding pipelines, vector search, prompt orchestration, and evaluation.
AI Agent Orchestration
Architecture for multi-step AI agents covering planning, tool use, memory, evaluation, and human-in-the-loop controls.