Building AI-Powered Applications
Part of the Prompt Engineering course
This is the capstone. Every earlier lesson taught one move: generating text, shaping structured output, calling functions, retrieving facts with RAG, running agents, and prompting for images. Real products are none of those alone. They are several of them wired together, with the plumbing that makes the whole thing trustworthy: orchestration, evaluation, guardrails, and a plan for when a call fails.
Up to now you have sent one prompt and read one answer. A product rarely fits in one prompt. Consider a product launch assistant: given a product name, it should produce a tagline, a price, and a hero image prompt, all consistent with each other. No single model call does that well. Instead you chain focused steps, each one the kind of prompt you already know how to write. ...
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Start this lessonBuilding AI-Powered ApplicationsWhat you'll learn
- Composing text, images, and agents into a workflow
- Orchestration and chaining steps
- Evaluation and guardrails
- Cost, latency, and failure handling
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All lessons in this course
11 lessons- 1What a Prompt Is20 min
- 2How Large Language Models Generate Text30 min
- 3The Five Principles of Prompting30 min
- 4Standard Practices for Text Generation35 min
- 5Prompt Templates and Function Calling35 min
- 6Vector Databases and Retrieval-Augmented Generation40 min
- 7Autonomous Agents: Memory and Tools40 min
- 8How Diffusion Models Generate Images30 min
- 9Standard Practices for Image Generation35 min
- 10Advanced Image Techniques: Inpainting, Outpainting, and ControlNet35 min
- 11Building AI-Powered Applications40 min
