Mission
Help a data or AI candidate build a portfolio artifact that proves they can turn an ambiguous AI idea into a useful, bounded product workflow.
Product boundary
This lab teaches a personal portfolio assistant for analyzing a single job description. It does not teach job aggregation, ranking, marketplace matching, recruiter workflows, or proprietary Dataaxy product logic.
Objective
Define a small role-fit assistant without building a job board, marketplace, scraper, or Dataaxy-style matching product.
Primary source
OpenAI frames prompting as the process of giving model input, and notes that output quality often depends on the prompt quality.
Start with a job-to-be-done
A strong AI engineering portfolio starts with a concrete workflow. In this course, the workflow is not "chat with my resume" and it is not "rebuild a job board." It is helping a candidate understand one role well enough to decide what to improve next.
The assistant should behave like a focused analyst. It reads a job description, extracts the work that matters, compares it to candidate evidence, and returns practical next steps.
That boundary matters. Dataaxy can later productize matching, job discovery, and recruiter workflows. The course should teach the transferable AI engineering skill without exposing or diluting the platform strategy.
- User: a data or AI candidate reviewing job postings
- Input: one job description and a lightweight candidate profile
- Output: role fit, missing evidence, and a next action
- Boundary: no scraping, no ranking marketplace, no recruiter-side automation
Design the first response contract
Before choosing a model or framework, define the shape of a useful answer. This gives you something testable, makes the assistant easier to evaluate, and keeps the project readable for recruiters.
For the first version, use a compact response with five parts: role snapshot, evidence from the job text, candidate strengths, missing proof, and a recommended next action.
This response contract turns the project from a demo into a product artifact. A recruiter can inspect whether the candidate understands evidence, constraints, and user value.
- Keep every section short enough to scan
- Use evidence from the job text instead of generic advice
- Prefer practical next actions over motivational copy
- Separate facts from recommendations
Make the prompt inspectable
A portfolio reviewer should be able to read your prompt and understand the product decision behind it. Avoid a prompt that simply asks the model to be helpful. Show the task, the evidence rules, and the response contract.
This is the first habit of applied AI engineering: design the behavior you want, then make the model operate inside that design.
- Task: compare a job posting with a candidate profile
- Evidence rule: quote the job text for every important claim
- Boundary: say "not enough evidence" when the job text is vague
- Output: use the same sections every time
Common mistakes
The most common mistake is making the assistant too broad. If it claims to search the market, rank every job, rewrite the resume, and decide whether to apply, it becomes impossible to evaluate and too close to a platform feature.
A second mistake is hiding the product decision inside a long prompt. A good portfolio project makes the trade-offs visible: what the assistant does, what it refuses to do, and why the workflow still creates value.
- Too broad: "Find me the best AI jobs"
- Better: "Analyze this one role against this evidence"
- Too vague: "Give career advice"
- Better: "Quote evidence, identify missing proof, suggest one next action"
Assistant brief checklist
- Name the user and the moment where the assistant helps
- State the input the assistant receives
- Define the answer format before writing prompts
- Include evidence rules so the answer is grounded in the job text
- Describe one failure mode the assistant must avoid
- State what the assistant intentionally does not do
Retrieval practice
Answer before you continue
- Who is the user, and what decision are they trying to make?
- What evidence should the assistant quote from the job description?
- What answer shape would make the output easy to compare across jobs?
- Which part of this idea would become a platform feature if Dataaxy built it internally?
Exercise
Write a one-page assistant brief, draft three prompts, and score the assistant output against a concrete review rubric.
Build the first assistant brief
Use one public job description and one lightweight candidate profile. Draft the assistant brief before writing any code.
- Write a one-sentence user story for the candidate workflow
- List the exact inputs the assistant can use and the inputs it must ignore
- Define a five-part answer format: role snapshot, evidence, strengths, gaps, next action
- Write one prompt that forces the assistant to cite evidence from the job text
- Add a "not enough evidence" rule for vague job descriptions
- Write a short product boundary note explaining why this is not a job board
Reveal the feedback checklist
- The user story names a concrete candidate decision
- The input fields are observable and not invented by the model
- The answer format can be reused on a second job posting without redesign
- The prompt asks for evidence instead of generic career advice
- The boundary note keeps Dataaxy platform logic out of the portfolio project
Artifact to ship
Role-fit assistant brief, prompt pack, and review rubric
Ask for feedback
If you are working with an AI coach or mentor, ask it to review your artifact against the checklist before moving to the next module.