A Prototype Before the Real Thing
Who this book is for
Người làm công nghệ và AI trong doanh nghiệp Việt Nam: nhân viên IT một mình hay trong nhóm nhỏ, lập trình viên, người dựng tính năng AI, trưởng nhóm kỹ thuật ở công ty vừa và nhỏ; vừa phải giữ hệ thống chạy ổn, vừa được giao đưa công cụ và AI mới vào công việc của các phòng khác.
Sound familiar?
- AI projects discover insufficient data after three months.
- Demos impress but break on real data.
- No criteria are set, so every prototype counts as a win.
- Prototypes go straight to production.
After reading, you can
Help AI builders test ideas fast and cheaply and know when to stop or continue.
Each chapter answers one question
Chapter 1: Is this prototype meant to prove or to learn?
What question must this prototype answer?
A good prototype answers a question; it does not prove an idea.
Chapter 2: The demo runs smoothly, but on whose data?
Am I testing with real data or data I picked myself?
A prototype run on nice data only proves the data was nice.
Chapter 3: Does a prettier prototype give a truer answer?
Which parts of the prototype am I allowed to do sloppily?
A prototype does not need to be pretty, only good enough to answer the question.
Chapter 4: Everyone loved the demo, so did it work?
Did my prototype pass or fail against the threshold set?
Prototype results must be read against numbers agreed in advance, not impressions.
Chapter 5: Is killing an idea after two days a loss?
After the prototype, should I stop, pivot or build it for real?
Stopping an idea after two days is a good result, not a failure.