Скачать или смотреть The MVP Prompt Formula: Role, Task, Constraints, Output

Most AI ideas fail not because of bad models, but because of poor prompts.
In this session, I break down how prompt engineering becomes the foundation for building real, usable MVPs with AI. This video introduces prompt engineering for Minimum Viable Projects (MVPs). It explains why prompt engineering is crucial, detailing a 4-layer prompt stack and a hierarchical approach to prompt engineering. Learn how to build an efficient minimum viable product while understanding common failure modes in prompt design, which is essential for any startup looking to achieve a successful mvp launch.

We explore how successful founders and researchers use prompt-driven systems to prototype faster, validate smarter, and communicate value clearly to users, stakeholders, and investors. Whether you are a student, founder, researcher, or professional, this session will help you think differently about AI product development.

If you are serious about building AI solutions that solve real problems rather than theoretical demos, this video is for you.

What you’ll learn:
How prompt engineering functions as product design
How to scope and validate an AI MVP
Why clarity beats complexity in AI systems
How to define success metrics for real users
Common AI MVP failure points and how to avoid them

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Timestamps
00:00 – Introduction and session overview
00:55 – What prompt engineering really means
01:40 – Why AI sounds confident but can be wrong
02:17 – Garbage in, garbage out explained
02:48 – How AI is creating solo founders and startups
03:15 – From idea to prototype to validation
03:44 – Translating ideas into prompt-driven systems
04:27 – Why most MVPs fail
05:44 – Prompt engineering as product design
06:22 – Focus on one core job, not everything
07:12 – Defining success metrics for your product
08:45 – The four-layer prompt framework
09:40 – Identifying real user problems
11:05 – Real-world MVP example from teaching experience
13:47 – Building what people will actually pay for
15:19 – Task definition and scope control
17:23 – AI use cases across industries
18:32 – Constraints, outputs, and system boundaries
20:57 – Prompt hierarchy and goal clarity
24:17 – Validating demand before building
26:51 – Avoiding reinvention and building on prior work
28:11 – Context provision and real-world constraints
29:22 – Execution and taking action
31:05 – Using SMART principles in prompting
33:12 – Designing for users, not yourself
35:16 – Avoiding assumptions and false confidence
38:25 – Testing, benchmarking, and deployment risks
41:09 – Common prompt engineering failures
45:20 – Avoiding infinite AI suggestion loops
46:23 – Using ground truth and examples
47:09 – Benchmarking against existing solutions

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