In this lesson
- 01Learning Objectives
- 02The Problem
- 03The Concept
- 04Use it
- 05Exercises
- 06Key Terms
- 07Further Reading
About 1500 words of reading, with code and the tutor.
The full lesson opens with an access code
You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.
Milestone forAn observable back-office agent
Schemas a model can use correctly
Write strict JSON schemas and descriptions for parse_statement, match_payment, get_invoice, draft_smart_invoice and send_whatsapp_template. Test tool selection on 30 requests and rewrite the descriptions that cause wrong picks.
Done when
- Strict schemas with enums and required fields for 5 tools
- Tool-selection accuracy on 30 requests, at least 90%
- Before and after results for every rewritten description
Stretch: Test the same schemas on a second model
Reviews open with an access code.
Interview practice
Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.
Scoring opens with an access code. You can still read the questions and prepare.
Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.