Module 07: Production AI Patterns

The gap between a working demo and a production AI system is enormous. Demos run on fast machines, with short prompts, and tolerant users. Production systems handle thousands of concurrent users, enforce strict latency SLAs, manage token budgets across tenants, and must recover gracefully from provider outages.

This module covers eight critical engineering patterns that separate amateur LLM integrations from production-grade AI systems.

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What You Will Learn

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Lesson Map

#	Lesson	Core Problem Solved	Key Techniques
01	Context Management at Scale	Context overflow, stale history	Sliding window, summarization, KV cache
02	Streaming Responses	Perceived latency, UX	SSE, chunked encoding, backpressure
03	Async LLM Calls	Throughput, concurrency	asyncio, task queues, fan-out
04	Batch Processing	Offline workloads, cost	Anthropic Batch API, polling, failure handling
05	Idempotency and Retries	Duplicate charges, flaky APIs	Exponential backoff, circuit breakers, fallback chains
06	Cost Optimization	Token spend, budget control	Prompt compression, caching, model routing
07	Multi-Tenant AI Systems	Tenant isolation, billing	Per-tenant rate limits, context isolation
08	AI Product Architecture	System design, integration	Event-driven AI, conversation store, vector store

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Prerequisites

• Python async programming (Module 03)
• REST APIs and HTTP fundamentals
• Basic familiarity with LLM APIs (Modules 01–06)

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Why Production Patterns Matter

"It works in the notebook" is not a deployment strategy.

Every pattern in this module was born from a real production failure: context overflows crashing long-running chat sessions, unbounded async tasks exhausting thread pools, missing idempotency keys generating duplicate charges, and token costs that grew 10x in a week because nobody tracked prompt length.

By the end of this module, you will have the engineering vocabulary and implementation skills to build AI systems that are reliable, observable, cost-controlled, and ready for real users.