RAG Application Blueprint
Reference architecture for retrieval-augmented generation apps covering embedding pipelines, vector search, prompt orchestration, and evaluation.
Components
Considerations
Alternatives
Complexity
Fit
When this blueprint fits
And when to walk away from it
When to use this
You need an LLM to answer questions grounded in a corpus of documents (product docs, internal knowledge, customer data) and you cannot fine-tune for every customer. RAG is the right answer when the data changes often and the model has not seen it.
When NOT to use this
If your domain is narrow, stable, and small enough to fit in a system prompt, RAG is overkill. Skip the vector store and inline the relevant context in the prompt.
Architecture
System components
Key building blocks of this architecture, layered from infrastructure up.
Document Ingestion
Embedding Pipeline
Retrieval Layer
Generation Layer
Evaluation Harness
Feedback Loop
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.
Need help implementing this blueprint?
I help teams adapt blueprints like this to their specific requirements and ship from planning through production.
AI Systems
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