A Common-Sense Guide to AI Engineering

Fragmented documentation, obsolete tutorials, and frameworks that deliver a prototype but flop in production can make AI engineering feel overwhelming. But it doesn’t have to be that way. With real-world code and step-by-step instructions as your guide, you can learn to build robust LLM-powered apps from the ground up while mastering both the how and why of the most crucial underlying concepts.

Harness context engineering and retrieval systems to create AI assistants that understand your proprietary data. Create chatbots that answer organization-specific questions and help solve users’ issues. Design agents that conduct research, make decisions, and take action in the real world. Level up your prompt engineering and get an LLM to do your bidding—-not its own. Use automated evals to keep constant tabs on your app’s quality while setting up guardrails to protect your users and organization. And implement observability systems that make it easy to debug your app when things do go wrong.

With a systematic approach grounded in the core principles of building AI apps for real users, you’ll easily evolve and adapt even as the hype and tools come and go.