FDE-2.02-Prompt-&-Context-Engineering

You'll receive a comprehensive, hosted-ready PDF guide that teaches you to engineer prompts and context windows for reliable, repeatable LLM behavior; the four-part Instruction–Context–Format template, system vs. user prompt separation, few-shot prompting, chain-of-thought reasoning, structured JSON output, temperature and constraint control, context-window curation, and prompt-injection awareness — paired with 9+ Mermaid diagrams, a full gotchas/troubleshooting section, and three progressively harder hands-on exercises built around real Anthropic API calls, culminating in your own working prompt-test harness.

Who it's for: learners who've completed FDE 2.01 (or already understand what an LLM and a token are) and early-career developers (SDE-1 or below) who can write basic Python but haven't been taught a systematic, testable approach to prompting — the difference between "it worked when I tried it" and "it works the same way every time."

Why it's valuable: every real AI integration lives or dies on whether the model's output is consistent enough for code to depend on, and this guide is the only beginner resource that treats prompting as an engineering discipline — with a reusable template, a test-set methodology, and explicit coverage of the structured-output and prompt-injection risks that most "prompt engineering" content skips entirely.

🚀 Getting Started (Day 1):

1. Confirm you're comfortable with FDE 2.01's basics (what an LLM and a token are) — this guide doesn't re-teach them
2. Sign up for a free Anthropic account at console.anthropic.com and generate an API key (free trial credits are enough for every exercise in this guide) — or start with the free Claude.ai chat UI if you'd rather begin conceptually
3. Run the one-line verification command in Section 2.6 to confirm your setup works, then read Sections 1–3 before touching the exercises

🛠️ Tools You'll Need:

• Free: A Claude.ai or ChatGPT account (chat-UI exercises), Python 3, VS Code, a terminal
• Free-tier friendly: An Anthropic API key (anthropic Python package) — new accounts receive trial credits that comfortably cover this entire module's exercises
• No paid software is strictly required to complete this module

📚 How to Practice:

• Work through the 3-tier hands-on exercises in Section 15 in order — guided walkthrough, then independent practice, then the mini-capstone test harness
• Each exercise includes exactly how to verify you succeeded, including deliberately testing edge cases like empty input — don't skip that step
• Use the Section 13 iteration discipline deliberately during the capstone — build your test set first, then revise and re-run the whole set, not just the failing case

💰 Value Proposition: Why ₹25 is Unbeatable

Market Comparison:

• Udemy "Prompt Engineering Masterclass" courses: ₹499–899
• Coursera Prompt Engineering Specialisations: ₹5,000–15,000
• AI consulting workshops: ₹15,000–50,000+
• 1-on-1 prompting mentorship: ₹2,000–5,000/hour
• This guide: ₹25 (one-time)

What You're Saving:

• ⏰ 10+ hours of scattered blog posts and Twitter threads on "prompt engineering tips"
• 💸 ₹2,800+ versus the cheapest paid alternative
• 🎯 A single focused sitting instead of a multi-week course
• ✅ A structured-output and prompt-injection framework you won't find in generic "how to talk to ChatGPT" content

ROI Example: One inconsistent classification prompt caught before it ships to a client — a malformed JSON response that crashes a parser, a prompt-injected customer email that leaks your system instructions — saves far more time and client trust than this entire guide costs. Your ₹25 pays for itself the first time it prevents one unreliable prompt from reaching production.