Generative AI Frameworks: Build Production AI Apps

Course Overview

This course teaches how to design and build production-ready generative AI applications by making informed architecture decisions. It covers selecting AI stacks for models, retrieval-augmented generation (RAG), agents, evaluation, deployment, and security, with a focus on defensible design choices and diagnosing failures. The course is aimed at Python developers and early career AI engineers who want to move beyond demos to reliable AI systems.

Key Takeaways

• Map AI product ideas into layered architectures with clear ownership and boundaries.
• Choose between hosted, self-hosted, or edge model inference based on multiple criteria.
• Specify typed answer contracts with citations and safe tool schemas to prevent unsafe executions.
• Diagnose errors in RAG pipelines by isolating failing stages.
• Use workflows, single-agent tools, and multi-agent teams with stop rules and human approval.
• Build evaluation sets with slices and gates to justify release decisions.
• Trace production incidents from user complaints to rollback decisions using observability tools.
• Manage long-running agents and optimize prompts automatically.

Prerequisites

• Comfortable writing Python functions and reading JSON.
• Ability to call HTTP APIs and interpret responses.
• Basic machine learning vocabulary helpful but not required.
• No GPU, API key, or local setup required.
• Willingness to do independent practical work on paper or diagrams.

Target Learners

• Python developers transitioning from scripts and demos to production AI systems.
• Early career AI engineers needing to justify architecture decisions.
• Backend and data engineers adding retrieval, tools, or assistants to existing products.
• Technical leads deciding when frameworks add value or cost.
• Anyone who has shipped prototypes that failed without clear explanations.