GenAI Architecture

A practical guide to designing, evaluating, securing, and operating production GenAI systems—from model and context fundamentals to RAG, agents, reliability, and rollout.

What is GenAI architecture?

GenAI architecture is the system design around a generative model: how the application selects context, retrieves private knowledge, calls tools, stores state, evaluates output, enforces permissions, and recovers from failure. The model is one component. A production system also needs deterministic software around it so that useful behavior is observable, testable, and safe to operate.

The right architecture depends on the job the system must do. A bounded extraction task may need one model call plus schema validation. A knowledge assistant may need retrieval and citations. A workflow with known steps should usually stay deterministic. An agent is appropriate only when the system genuinely needs to choose tools or decide its next step at runtime.

Production GenAI architecture at a glance

Choose the simplest architecture that works

1. Direct model call — generation, classification, extraction, or transformation with bounded input.
2. RAG — answers require private, current, or citable knowledge.
3. Deterministic workflow — the steps are known and the application should control transitions.
4. Agentic workflow — the system must choose tools or adapt its path dynamically.
5. Human approval — an action is irreversible, high-risk, or requires accountable judgment.

Complexity is not a maturity signal. Every additional model decision creates another behavior to evaluate, trace, secure, and recover. Start with the least autonomous pattern that satisfies the requirement, then add retrieval, tools, memory, or autonomy only when a concrete need justifies it.

What this guide covers

• Foundations: how GenAI systems differ from traditional software and what makes a system agentic.
• Building blocks: models, prompts, context, retrieval, tools, MCP, state, memory, identity, and permissions.
• Architecture patterns: direct LLM calls, RAG, deterministic workflows, agents, multi-agent systems, and human review.
• Reliability: evaluation, threat modeling, guardrails, observability, governance, and readiness checks.
• Production engineering: specifications, deployment, data, CI/CD, scaling, cost, rollout, and operational ownership.

How to use this guide for an interview

Do not memorize one reference diagram. Practice explaining why a simpler pattern is insufficient, where nondeterminism enters the system, what evidence you would collect, and how the system fails safely. A strong GenAI system-design answer connects architecture choices to quality, latency, cost, permissions, rollout, and measurable user outcomes.

Start with the foundations, then use the pattern-selection chapters to compare direct calls, RAG, workflows, and agents. When you can explain the trade-offs, apply them in the architecture exercises and mock interviews.