Prototype in revision · Applied AI workflow
A product-style experiment in turning unstructured information into a bounded, useful AI workflow.
Project story
The setting
A job seeker may have a job description, resume, target company, role context, and personal notes spread across different places. The challenge was not generating more text; it was creating a guided path from raw material to something a person could actually practice.
Why now
I wanted a public, self-serve project that showed how an AI capability could be shaped into a bounded product experience rather than presented as an open-ended chatbot.
The tension
The workflow had to be useful without pretending to know more than the input supported. It also had to be simple enough to try, controlled enough to operate publicly, and clear enough that a user could see where AI helped and where human judgment remained necessary.
People in the system
Perspective
A clear path from a job description and personal experience to focused practice, not a large block of generic advice.
Perspective
A sequence of steps, prompts, and outputs that makes the AI's role understandable and keeps the user moving.
Perspective
Reasonable usage boundaries, reliable deployment, and enough structure to improve the workflow based on real use.
The journey
The workflow accepts the job description, target role, company context, and resume information that a person already has, then organizes it around the preparation goal.
Instead of hiding the prompt work behind one button, the experience gives the user a sequence: understand the target, identify likely themes, generate practice, and review the result.
The output is shaped around interview preparation rather than unlimited content generation, which makes it easier to review, refine, and compare with the source material.
Public deployment, usage limits, user flow, and future provider integration are treated as part of the project—not as details that begin after the demo is finished.
Decisions that shaped it
Decision
The product value comes from the sequence of tasks and decisions around the model, not only from the model's ability to produce fluent text.
Decision
The user remains responsible for choosing what is accurate, relevant, and worth practicing. AI helps organize and expand the material; it does not silently become the user's history.
Decision
Deployment, usage limits, provider choices, and a clear public entry point determine whether a useful idea can actually be tried by another person.
Ways to read the work
Product lens
The experience is designed around the user's next useful step: understand, practice, review, and improve.
Delivery lens
A usable demo needs a clear entry point, bounded interaction, visible output, and an iteration path—not only a technically interesting model call.
Systems lens
Inputs, prompts, outputs, limits, deployment, and future provider integration form a small but understandable operating model for the experiment.
What changed
User journey
Structured input becomes a multi-step interview-preparation experience rather than an unbounded prompt box.
Product evidence
The project makes prompt framing, user flow, output boundaries, deployment, and iteration visible enough to discuss.
Business outcome
A public demonstration of how applied AI can turn unstructured information into a focused, useful workflow.
Reflection
A convincing AI demo is not the one that says the most. It is the one that helps someone take a better next step and understand why the result is useful.
Project notes
I built it as a product-style experiment: define the user journey, turn scattered information into a structured preparation flow, set sensible boundaries for usage, and make the result easy to try in public.
The public link is paused while a revised version is being prepared.