Build Reliable Intelligent Systems That Improve Real Work
Take a messy real-world workflow, decide whether AI is useful, design a reliable intelligent system, build it, evaluate it, deploy it responsibly, and improve it with feedback.
After finishing this course, a learner should be able to:
- analyze a real workflow;
- decide when not to use AI;
- design system boundaries;
- build AI-ready software foundations;
- organize data, documents, and state;
- use models as bounded components;
- build grounded retrieval systems;
- work with multimodal documents and human correction;
- connect AI to tools, workflows, approvals, and agents;
- evaluate quality, cost, latency, traces, and failure modes;
- deploy, roll back, and improve AI systems responsibly;
- reason about security, privacy, governance, and economics;
- ship proof of useful capability through portfolio-grade projects.
- 46 chapters across 9 ordered parts.
- 9 guided build projects, one after each major capability.
- 1 capstone that proves end-to-end AI engineering capability.
- Chapters teach the mental model, mechanics, examples, mistakes, design decisions, build artifact, recall questions, summary, and next-step preview.
- Projects are build-ready specifications, not exercises. Each project is scoped only to concepts already taught.
- The capstone asks the learner to choose one meaningful workflow, build a narrow system, evaluate it, review risk, and make a product-real launch decision.
Each chapter ends with a proof artifact. No chapter ends with only "understanding."
The guided projects are the portfolio path through the course:
- Problem-first workflow analysis.
- AI-ready app skeleton.
- Knowledge base search engine.
- Structured model extraction.
- Grounded RAG assistant.
- Multimodal document analyst.
- Tool and agent workflow assistant.
- Production AI control center.
- Risk, governance, and economics review.
Start here: Project Guide
- License: Apache License 2.0
- Contributions: Contributing Guide
The project is open source under Apache-2.0. Quainy names, logos, and brand assets are not licensed for reuse except for reasonable attribution as described in the contributing guide and notice.
Capability: decide what to build and why.
- AI Engineering Is System Design
- When Not to Use AI
- From Messy Workflow to System Boundary
- Metrics, Baselines, and Meaningful Impact
Project: Support Workflow Intelligence Console
Capability: understand where AI components live inside real software.
- AI Application Architecture
- APIs, Services, Workers, and Queues
- Auth, Permissions, Files, and Storage
- Async Jobs, Background Processing, and Webhooks
Project: AI App Skeleton
Capability: turn messy knowledge into usable system context.
- Data, Documents, and State
- Data Ingestion, Parsing, and Cleaning
- Schemas, Contracts, and Structured Outputs
- Data Quality, Lineage, and Versioning
- Search, Ranking, and Information Architecture
Project: Knowledge Base Search Engine
Capability: use models with understanding, not worship.
- What Language Models Actually Do
- Tokens, Context, Attention, and Generation
- Prompting as Interface Design
- Model Selection, Routing, and Fallbacks
- Fine-Tuning, Adaptation, and When Not to Tune
- Model Failure Modes and Error Analysis
Project: Structured Intake Extractor
Capability: build systems that answer from trusted knowledge.
- Embeddings and Semantic Retrieval
- Chunking, Indexing, and Retrieval Pipelines
- Retrieval-Augmented Generation
- Context Engineering and Grounding
- Evaluating Grounded AI Systems
- From Assistant Demo to Knowledge Product
Project: Grounded Research Assistant
Capability: handle documents, images, audio, realtime inputs, and human correction loops.
- Document AI: OCR, Tables, Forms, and PDFs
- Vision, Image, and Multimodal Inputs
- Audio, Speech, and Realtime AI
- AI UX, Trust, Feedback, and Human Correction
Project: Multimodal Document Analyst
Capability: build AI systems that take bounded action.
- Tool Use and API Actions
- Connectors, MCP, and Tool Registries
- Workflow Orchestration
- Memory, State, and Human Approval
- Computer Use and Browser Automation
- Agents Without Hype
- Agent Evaluation and Trace Review
Project: Operations Action Assistant
Capability: measure, observe, deploy, and improve AI systems without guessing.
- Evaluation as Product Infrastructure
- Observability, Traces, Cost, and Latency
- Caching, Streaming, Batching, and Inference Optimization
- LLMOps: Versioning Prompts, Models, Evals, Data, and Indexes
- Deployment, Rollbacks, Feedback, and Continuous Improvement
Project: Production AI Control Center
Capability: make AI systems safe, governed, economically justified, and worth keeping.
- AI Security Threat Model
- Prompt Injection, Retrieval Poisoning, and Tool Abuse
- Privacy, PII, Data Retention, and Access Control
- Responsible AI, Red Teaming, and Risk Management
- AI Product Economics: Cost, ROI, Build-vs-Buy
Project: AI Risk and Governance Review
Final proof: choose one real workflow, build one narrow intelligent system, evaluate it, analyze failure, document risk, estimate economics, and show deployment readiness.