GenAI Reference Architectures

The most fundamental GenAI pattern: a single stateless LLM call with a system prompt. Every generative AI application starts here. Understand request/response flow, prompt design, temperature tuning, token limits, streaming, and error handling.

Multi-turn chat with memory and session management. The chatbot remembers previous messages, maintains context across turns, and manages conversation state — the backbone of every conversational AI product.

Retrieval-Augmented Generation grounds LLM responses in your own data. Instead of relying solely on the model's training knowledge, RAG retrieves relevant documents from a vector database and injects them as context — dramatically improving accuracy, reducing hallucination, and enabling real

Automated document ingestion and structured extraction at scale. Parse PDFs, images, and raw text into clean data, then use LLMs to summarize, classify, and extract structured entities — producing reliable JSON output ready for downstream databases and APIs.

Intelligently route requests to different LLMs based on task complexity, cost, and latency requirements. Use cheap, fast models for simple tasks and reserve expensive, capable models for complex reasoning — cutting costs by 60-80% without sacrificing quality where it matters.

A single LLM agent that reasons about a user request, selects from a set of registered tools via function calling, executes them in a loop, and synthesizes results into a final answer. This is the foundational pattern for giving LLMs the ability to take real actions in the world — searching the web,

Protect your LLM application with input/output validation, safety filters, and automated quality evaluation. Guardrails catch prompt injection, PII leaks, toxic content, and malformed outputs before they reach users. LLM-as-judge evaluation enables continuous quality monitoring without manua

Fine-tune and deploy custom models tailored to your domain. Learn when prompt engineering hits its ceiling and how to adapt a base model with your own data — from data preparation and LoRA training through evaluation, registry management, A/B deployment, and continuous quality monitoring.

Orchestrate multiple specialized AI agents that collaborate to solve complex, multi-step problems. Each agent has a defined role, tool access, and communication protocol — coordinated by an orchestrator that plans, delegates, synthesizes results, and handles failures gracefully.

The capstone architecture: an end-to-end enterprise GenAI platform that unifies every pattern from this series. API gateway with auth and rate limiting, intelligent request routing, RAG and agent pipelines, multi-model pool, guardrails, semantic caching, observability, cost tracking, and com