Week 3 · Foundations

 Prompting & Context Engineering .

 How to make a probabilistic model behave like a reliable business tool . Last week you drove a coding agent and saw its harness. This week we zoom into one layer — the instruction layer — because it is the highest-leverage, lowest-cost skill in the entire stack.

 This week: 📝 Quiz 1 (Weeks 1–3 foundations, 5 multiple choice, closed-book) at the end of class — readings and sample questions are on the Week 2 Summary page . 👥 GA1 is due Tue 9/15 (Wed section) / Wed 9/16 (Thu section), 11:59 pm — join a group if you haven’t. 📡 Agent Radar opens class.

 The hook

 One prompt change flips reliability

 Same model. Same 20 messy vendor emails. The only thing that changes is the prompt. (Illustrative teaching example — you’ll reproduce this effect yourself in the hands-on.)

 Before

 What’s the total?

 Usable outputs: sometimes a number, sometimes a sentence, sometimes the wrong currency… 
 ~60% usable · format varies every run 

 After

 SYSTEM: You are an invoice-processing assistant.
Return ONLY JSON: {"total_usd": number|null}
If no total is present, use null.

EXAMPLE
input: "…amount due USD 1,284.50…"
output: {"total_usd": 1284.50}

 Same model — now every answer is a clean, parseable record. 
 ~100% usable · identical shape every run 

 The gap between ~70% and ~98% reliability is the gap between a tool people trust and a tool people turn off. Prompting is free and instant — it’s where an MIS professional adds value without an ML PhD.

 The cost lever

 Cheapest fix first

 When an LLM feature underperforms, there is an ordered menu of fixes — and the price rises steeply as you go down it.

 Fix Cost Speed When 

 1 · Edit the prompt Free Seconds Always start here 

 2 · Add few-shot examples Free (a few tokens) Minutes Format or edge-case misses 

 3 · Better context / retrieval Engineering time Days Model lacks the facts (Week 7: RAG) 

 4 · Fine-tune Data + compute + upkeep Weeks Consistent style at scale, hundreds+ examples 

 5 · Wait for a new model — Quarters Not a strategy 

 This week

 Work through the four parts

 01 
 Foundations Anatomy of a prompt The five parts every prompt has — click through an annotated real example — plus system vs user prompts and why the system prompt is your policy guardrail. 
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 02 
 Reliability Techniques that raise reliability Few-shot, chain-of-thought, structured JSON output, and a temperature dial you can drag — with a which-technique picker to test yourself. 
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 03 
 The 2026 core skill Context engineering The window is a budget. Play the packing game: fit a support agent into 4,000 tokens — and meet context rot, plus the week’s news hook. 
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 04 
 Practice Exercises, case studies, interview check Prompt Autopsy, the hands-on with a reliability check, the $1 Chevy Tahoe and friends, your capstone system prompt, and five interview questions. 
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 Objectives

 By the end of Week 3 you can…

 Name the five parts of a prompt and use each as a knob 
 Apply system prompts, few-shot examples, chain-of-thought, and JSON output 
 Set temperature to match the task — low to automate, high to ideate 

 Explain context engineering: the budget, curation, and context rot 
 Decide when to prompt vs when to fine-tune (and why fine-tuning comes last) 
 Prove a prompt change actually improved results — with a reliability check