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Overview

By the end of this module, you’ll be able to:
  • Understand how LLMs process context and why it matters for production
  • Design structured prompts that often reduce hallucinations (results vary by task/model)
  • Apply advanced techniques like Chain-of-Thought and extended thinking
  • Select appropriate models and optimize costs through caching
  • Test and iterate on prompts systematically
Why This Matters: Most Gen AI projects fail not because of model limitations, but because of poor prompt engineering. The difference between a $5.000 monthly OpenAI bill and a $500 monthly bill often comes down to how you structure your prompts. We’ll learn production patterns that actually work, backed by real-world data. 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. Real-world impact: Teams using these patterns report 50-70% cost reductions from caching and 10-20% accuracy gains from structured prompting.