AI Engineering 101, Volume 1: Foundations, Setup & Your First RAG
Learn to design production AI systems the way senior engineers do. Ten modules take you from the systems fundamentals every engineer needs - latency, caching,…
Category: Technology · Level: all_levels · 84 lessons · Price: USD 99.00
Curriculum
Start Here: Primers and Glossary (read what you need)
- How to Use This Section: Which Primers You Need
- Primer 1: How Apps and the Internet Work
- Primer 2: How an LLM Product Works
- Primer 3: Hardware and Units
- Primer 4: Maths and Statistics You Need
- Primer 5: Reading Python Code
- Primer 6: How Cloud Services Run (and Ops Vocabulary)
- Primer 7: Reading Academic and Technical Sources
- Primer 8: GPUs in One Page
- Course Glossary
Module 0: Setup for PC and Mac, Python, venv, Git, Keys, First LLM Call
- Where This Came From: Why Every Python Project Gets Its Own Box
- Your First Terminal: Opening It, Moving Around, Two Terminals and PATH
- Installing Python 3.14 on Windows and on Mac (Including Without Admin Rights)
- Your Editor: VS Code, Saving .py Files Safely, and an Optional Coding Agent
- Virtual Environments, pip and requirements.txt (Plus uv and a Locked-Down Windows Route)
- Git and GitHub: Install, Sign In over HTTPS (No Admin Needed), Optional SSH
- Secrets from Day One: .env, .gitignore and Never Committing a Key
- Your First LLM Calls: a Local Model with Ollama and a Hosted Model with an API Key
- A Gentle Primer: async, HTTP and Your First FastAPI Service
- Hands-on Lab: Run the Machine-Ready Checker
- Troubleshooting: the Errors Everyone Hits (Windows and Mac)
- Design Exercise: Setup Instructions a Whole Team Can Follow (with Model Answer)
- Portfolio Step 0: Your Course Repository on GitHub, with README and pyproject.toml
- Interview Corner: Five Setup and Fundamentals Prompts
- Module 0 Reading List
- Module 0 Quiz: Setup for PC and Mac
Module 1: Thinking in Systems
- Where This Came From: From Batch Jobs to Planet-Scale Services
- Latency, Throughput and Why Averages Lie
- Back-of-the-Envelope Estimation: Sizing an AI Study Assistant
- Queues and Little's Law: Why Systems Fall Off a Cliff
- SLIs, SLOs and Error Budgets: with the OpenAI December 2024 Outage
- Hands-on Lab: Load-Test a Toy AI API and Find the Knee
- Design Exercise: The Exam-Week Study Assistant (with Model Answer)
- Portfolio Step 1: Scaffold the Study Assistant API and Write Its SLO Doc
- Interview Corner: Six System-Design Prompts
- Module 1 Reading List
- Module 1 Quiz: Thinking in Systems
Module 2: Core Building Blocks
- Where This Came From: From One Big Server to Caches, Queues and NoSQL
- Load Balancers and CDNs: Sharing the Work and Moving It Closer
- Caching: Remembering Answers Without Getting Burned
- Queues and Streams: Doing Work Later, Safely
- Databases: SQL, NoSQL, Indexes and Sharding
- Hands-on Lab: Add an Answer Cache and a Job Queue, Then Break Them
- Design Exercise: The Term-Start Notes Rush (with Model Answer)
- Portfolio Step 2: Document Your Cache and Queue Like an Engineer
- Interview Corner: Five Building-Block Prompts
- Module 2 Reading List
- Module 2 Quiz: Core Building Blocks
Module 3: Prompt and Context Engineering
- Where This Came From: From GPT-3's Few-Shot Trick to Context Engineering
- Anatomy of a Prompt: System Prompts, User Prompts and Clear Instructions
- Show, Split and Think: Few-Shot Examples, Breaking Tasks Down and Room to Reason
- Structured Outputs: Getting JSON Your Code Can Trust (and a First Look at Function Calling)
- Context Engineering: The Context Window as Working Memory, Caching and Prompt Injection
- Prompts Are Code: Versioning and Testing Against a Test Set
- Hands-on Lab: Take a Triage Prompt from 0% to 100%, Then Prove It on Held-Out Questions
- Design Exercise: The Triage Step for 120,000 Students (with Model Answer)
- Portfolio Step 3: One Model Switch, a Versioned Triage Prompt and an Eval Gate
- Interview Corner: Five Prompting and Evaluation Prompts
- Module 3 Reading List
- Module 3 Quiz: Prompt and Context Engineering
Module 4: Secrets from Day One (SecretCarousel)
- Where This Came From: From Passwords in Code to Agent-Scoped Vaults
- How Secrets Actually Leak: and Two Breaches That Started With One Key
- Blast Radius, Least Privilege and Secret Zero
- Rotation, Revocation and the Leak Playbook
- Secret Scanning: Smoke Alarms for Your Repository
- SecretCarousel: the Vault Model in Practice (and What It Costs)
- Portfolio Step 4: Fetch-at-Use Secrets for the Study Assistant
- Hands-on Lab: The Leak Drill, Then Move Your Key Into the Vault
- Design Exercise: Securing StudyBot's Secrets (with Model Answer)
- Interview Corner: Five Secrets-Management Prompts
- Module 4 Reading List
- Module 4 Quiz: Secrets from Day One
Module 5: Your First RAG, from BM25 to Cited Answers
- Where This Came From: From Library Catalogues to RAG
- Keyword Search by Hand: TF-IDF, BM25 and Measuring Recall
- Meaning as Numbers: Embeddings and Cosine Similarity
- Chunks, Vector Stores and Hybrid Search
- Grounded Answers: Prompts, Citations and Saying "I Couldn't Find This"
- Hands-on Lab: Build Your First RAG, Step by Step
- Lab Resources: Sample Notes and Test Questions
- Design Exercise: A Course Assistant for 600 Students (with Model Answer)
- Portfolio Step 5: Add /ask/notes to Your Study Assistant
- Interview Corner: Five RAG Questions
- Module 5 Reading List
- Module 5 Quiz: Your First RAG
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