PRODUCTION AI
AI Engineering
Learn how to turn AI models into reliable, useful and maintainable software systems that work in the real world.
AI Engineering Learning Path
Follow the modules in order to understand the decisions, practices and systems needed to build dependable AI applications.
What Is AI Engineering?
The role, its boundaries, and what an AI Engineer actually builds and owns in production — the first step from 'I understand AI concepts' to 'I can build reliable AI systems.'
How AI Applications Differ from Traditional Software
The structural reasons non-determinism, model dependency, and evaluation difficulty require a different engineering mindset — and why unit testing alone falls short for AI behavior.
AI Application Architecture
The complete, layered system design — client through observability — that every subsequent module in this course references by name. Every layer's responsibility, and what breaks without it.
Model Selection Framework
Level 2 begins here: a repeatable decision framework for choosing between small and large models, reasoning models, open-source vs. proprietary, and hosted vs. self-hosted.
Prompt Engineering as Software Engineering
Treating prompts as production artifacts — templates, versioning, testing, and defense against injection — not throwaway strings scattered through application code.
Context Engineering
Closing Level 2: why context is one of the most important, scarce resources in an AI system, and how to select, compress, order, and prioritize it deliberately — distinct from prompt engineering.
Production RAG Engineering
Level 3 begins here: building on your RAG course's foundations to cover the production-specific concerns — caching, observability, failure diagnosis, and when NOT to use RAG at all — that a working demo doesn't need but production does.
AI Agent Engineering
Building on your Agents course to cover production-specific concerns: tool reliability, retries, loop prevention, permissions, sandboxing, and — critically — when a deterministic workflow beats an autonomous agent.
Tool Calling & Structured Output
Closing Level 3: the complete engineering lifecycle of a tool call, and how unreliable free-form text becomes reliable, parseable software input.
AI Evaluation Deep Dive
Level 4 begins here — one of the deepest topics in this course: what it means for an AI system to be 'correct,' golden datasets, human eval, LLM-as-judge, and a complete evaluation pipeline.
LLM Evaluation Framework & Lifecycle
Closing Level 4: offline vs. online evaluation, regression/A-B/canary/shadow testing, and the complete production evaluation lifecycle a AI system change moves through before full rollout.
Observability
Level 5 begins here: AI-specific observability beyond traditional logs and metrics — prompt tracing, token usage, per-stage latency, and debugging one failed AI request end-to-end.
AI Security
A comprehensive treatment of prompt injection, data leakage, tool abuse, excessive agency, and RAG poisoning — security as a architecture, not a list of attacks to memorize and defend against individually.
AI Reliability
Closing Level 5: why AI systems fail — model/provider/network failures, timeouts, hallucinations — and the reliability patterns (retries, circuit breakers, fallbacks, graceful degradation) that keep a system functioning when a dependency inevitably breaks.
AI Cost Engineering
Level 6 begins here: what makes AI applications expensive, and the concrete techniques — token budgets, semantic caching, model routing, batching — that control it before the monthly bill becomes a surprise.
AI Latency Engineering
Where latency comes from across the full request pipeline, and the techniques — streaming, parallel execution, caching, model routing — that keep response times within a real, defined budget.
Scalability
Closing Level 6: how AI applications scale from 10 requests a day to millions, and the infrastructure patterns — queues, async workers, connection pooling, horizontal scaling — that make that growth possible.
Data Engineering for AI
Level 7 begins here: AI applications are data-dependent — ingestion pipelines, validation, versioning, lineage, and governance for the data that feeds retrieval, evaluation, and feedback loops.
AI Application Memory
Different types of memory — short-term, long-term, semantic, episodic — and when memory is useful versus when it adds unnecessary complexity to a system that doesn't need it.
Human Feedback & Model Improvement Strategy
Closing Level 7: how user feedback flows back into improving prompts, retrieval, and models — and the systematic optimization hierarchy for deciding what to change first when a system underperforms.
Fine-Tuning vs. RAG vs. Prompting
Level 8 begins here: a practical decision framework for choosing between these three approaches — and when NOT to use any of them, since traditional deterministic code sometimes wins outright.
AI Workflow vs. AI Agent
Comparing deterministic workflows, LLM workflows, and agents across reliability, cost, latency, and control — directly extending your Agents course's 'least autonomous architecture' principle into a concrete decision framework.
Architecture Decision Making
Closing Level 8: the complete, senior-engineer decision framework spanning scale, latency, cost, security, reliability, data sensitivity, and team expertise — with decision matrices for real trade-offs.
AI Testing
Level 9 begins here: unit, integration, contract, prompt, model, evaluation, RAG, agent, and chaos testing — and a precise map of what can and cannot be made deterministic in an AI system's test suite.
Deployment
Local, Docker, cloud, serverless, and GPU deployment options; hosted vs. self-hosted serving; and the blue-green and canary deployment strategies that turn Module 11's evaluation-gated lifecycle into real, running infrastructure.
AI CI/CD
How AI changes the traditional CI/CD pipeline — prompt tests, evaluation tests, dataset validation, and regression gates — assembled into one complete pipeline that mirrors Module 11's deployment lifecycle.
LLMOps / AI Ops
Closing Level 9: LLMOps vs. MLOps, unified model/prompt/dataset registries, experiment tracking, and the complete AI system lifecycle from development through governance.
AI Architecture Patterns
Level 10 begins here: reusable, named architecture patterns — simple LLM app, RAG, RAG+reranking, agent+tools, human-in-the-loop, multi-agent, enterprise knowledge assistant — and exactly when each one applies.
AI Anti-Patterns
Closing Level 10: over 50 mistakes and anti-patterns across architecture, reliability, security, cost, and evaluation — why each happens, why it's dangerous, and the correct approach, each traced back to the module that covers the fix.
Complete Production AI System
Level 11 begins here: a full enterprise AI application design, from frontend through CI/CD, with every component explained and a complete request-lifecycle trace showing every module in this course working together.
Failure Engineering
Closing Level 11: intentionally breaking a production AI system across 15+ realistic scenarios — LLM/vector-DB/tool unavailable, hallucination, injection, infinite loops, cost explosion — with the exact expected system response for each.
The AI Engineering Career
Level 12 begins here: the junior-to-architect progression, what differentiates senior engineers, how to think about and design AI systems from scratch, and what interviewers actually expect.
WHY AI ENGINEERING?
Build systems, not just model demos
AI Engineering connects models with software architecture, data, evaluation, observability, security and operations. These practices help teams create AI applications that remain useful beyond the first successful experiment.