Overview
Getting a large language model to answer a prompt is easy. Getting it to behave like a dependable software component is not: models return prose when you need data, fail in ways ordinary code never does, and sit behind rate-limited APIs with real latency and real cost. The gap between a working demo and a production LLM feature is exactly the set of patterns this course teaches.
This is a hands-on, advanced course. It starts with the judgment layer: a clear mental model of where LLM applications actually fail and which patterns earn their complexity, because the most common advanced mistake is adding machinery a simpler design did not need. From there it layers up one pattern at a time: structured output you can validate, tool and function calling, streaming responses, and the resilience engineering that keeps a feature working when the model or the API misbehaves. The course deliberately covers fewer patterns than a survey would and goes deep on each, grounded in the Claude and OpenAI APIs. Every module includes a lab, and each module builds on the one before it.
Who Should Attend
- Developers who have built a working LLM application and need it to hold up in production
- Engineers responsible for LLM features that other systems and teams depend on
- Technical leads setting patterns and standards for generative AI development
Learners who have not yet built an LLM-backed application should take Building Generative AI Applications first.
Prerequisites
- Experience building an application that calls an LLM API (Claude, OpenAI, or similar)
- Working proficiency in Python or TypeScript
- Comfort with REST APIs, JSON, and asynchronous code
What You Will Learn
- Judge which advanced patterns a given LLM feature actually needs, and which it does not
- Design structured output with schemas, validation, and repair strategies
- Build tool and function calling loops the model can drive reliably
- Implement streaming responses, including streaming with structured output and tools
- Engineer for failure: timeouts, retries, rate limits, fallbacks, and graceful degradation
- Harden a complete LLM feature for cost, latency, and testability
Course Outline
Day one: from working call to reliable component
- Thinking in Production Patterns
- Where LLM applications really fail: a taxonomy of model, API, and integration failures
- The anatomy of a production LLM feature, and the cost of each added pattern
- Deciding what your feature needs: a decision framework used throughout the course
- Lab: audit a naive LLM integration and rank its failure points by likelihood and impact
- Structured Output
- Why prose is not an interface: getting JSON you can trust
- Schemas and structured response modes in the Claude and OpenAI APIs
- Validation, repair loops, and deciding when to retry versus fail
- Lab: convert a free-text feature to schema-validated output with a repair path
- Tool Use and Function Calling
- Defining tools the model can call well: names, descriptions, and parameters
- The tool call loop: request, execute, return results, continue
- Handling tool errors and results the model did not expect
- Lab: build a feature where the model chooses among several tools to answer a request
Day two: behavior under real conditions
- Streaming
- Why streaming matters for perceived latency and user experience
- Server-sent events and streaming client patterns
- Streaming combined with structured output and tool calls
- Lab: convert a blocking endpoint to a streamed response with progressive rendering
- Robust Error Handling and Resilience
- Timeouts, retries with backoff, and rate limit handling
- Fallback strategies: alternate models, cached answers, and honest degraded modes
- Idempotency and making LLM calls safe to repeat
- Lab: inject failures into a working feature and make it degrade gracefully
- Hardening a Complete Feature
- Composing the patterns: what a finished production feature looks like
- Cost and latency budgets, and where each pattern spends them
- Testing nondeterministic behavior, and the handoff to systematic evaluation
- Lab: take one feature through a hardening checklist and defend each decision
Extended Version
The three-day version keeps the same gradient and adds room to go deeper:
- Multi-provider design: abstracting across Claude and OpenAI, and gateway patterns
- Caching strategies, including prompt caching, and their cost and correctness tradeoffs
- Deeper evaluation of hardened features, connecting to Evaluating and Monitoring Generative AI Applications
- A capstone that carries one LLM feature from naive prototype to a hardened, tested, production-ready implementation