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Module Overview

You’ve probably noticed: LLM prototypes seem promising but break when real data, cost, and reliability constraints show up. Here’s why that happens: Prompts are unstructured, context is unmanaged, and models are asked to do work better handled by deterministic systems. In this module: You’ll learn patterns to structure prompts, reduce hallucinations, and lower costs without sacrificing quality — backed by real-world production data.

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Learning Objectives

By the end of this module, you will be able to:
  • ✅ Understand how LLMs process context and why it matters for production
  • ✅ Design structured prompts using XML tags, few-shot examples, and JSON schemas
  • ✅ Apply advanced techniques: Chain-of-Thought, self-consistency, prompt chaining
  • ✅ Select appropriate models and optimize costs through caching and cascading
  • ✅ Test and iterate on prompts systematically with evaluation datasets
  • ✅ Implement prompt security defenses against injection, jailbreaking, and data leakage

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Why This Matters

Most GenAI projects fail not because of model limitations, but because of poor prompt engineering:
  • Cost: The difference between a $5,000 and $500 monthly bill often comes down to prompt structure — caching, cascading, and token optimization
  • Reliability: Unstructured prompts produce inconsistent results. Structured prompts with schemas and examples reduce hallucinations significantly
  • Security: Without guardrails, LLMs are vulnerable to prompt injection, data leakage, and jailbreaking — all preventable with the right patterns
  • Debugging: When a prompt fails in production, you need systematic evaluation — not “try a different wording”

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What You’ll Build

  • Interactive LLM Playground — test prompts with different models and temperatures
  • Structured outputs — extract data reliably using JSON mode and XML schemas
  • Prompt chains — break complex tasks into debuggable, sequential steps
  • Security defenses — prompt injection detection, PII filtering, jailbreak prevention
  • Cost optimization — model cascading and prompt caching strategies