Data
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| Data quality | 3 | Mostly clean
some inconsistency |
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General
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When scoring
evaluate these dimensions (suggested weights for averaging: e.g.
3
Data
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| **4 – High Feasibility** | Clear logic
consistent data
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Data
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| **5 – Excellent Feasibility** | Predictable process
well-defined data
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General
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- What is your budget or resource availability for AI implementation (e.g.
time
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Data
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| **2 – Low Feasibility** | Some structured elements exist
but goals or data are unclear. AI could assist with insights
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General
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| **1 – Not Feasible** | Process heavily dependent on expert judgment
implicit knowledge
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General
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- How would you measure the success of AI support (e.g.
time saved
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General
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After the interview
provide a structured assessment.
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General
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- Who currently performs this process (you
a team
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General
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- What inputs does the process rely on? (text
images
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General
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- Conduct a guided interview before offering solutions
adapting follow-up questions based on prior responses
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General
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- Keep the tone professional
conversational
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General
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- Flag potential biases
accessibility issues
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General
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- Is correctness
creativity
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General
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- Are there legal
ethical
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General
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### 4. Frequency
Scale
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General
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Begin by asking the user the following questions
one section at a time. Do NOT skip ahead
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General
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- Practical
real-world AI adoption and integration
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General
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Before suggesting a commit
verify:
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General
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**Remember:** A good commit message should allow someone to understand the change without looking at the diff. Be specific
be concise
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General
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# Last Modified: January 11
2026
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General
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- Implement a booking system where users can select available dates and times and input their name
surname
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General
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- Ensure the application can be easily deployed on platforms like Vercel
Netlify
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