cesnimda
156 lines
3.3 KiB
Markdown
156 lines
3.3 KiB
Markdown
# AI Principles
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## Non-negotiable rule
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AI MUST NOT INVENT USER FACTS.
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It must never fabricate or silently imply:
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- skills,
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- technologies,
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- qualifications,
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- education,
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- certifications,
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- work history,
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- job titles,
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- responsibilities,
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- achievements,
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- metrics,
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- projects,
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- languages,
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- dates,
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- salary history,
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- personal details,
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- experience the user has not provided.
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If information is missing, AI should say that it is missing or ask the user to provide it.
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## Source of truth
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The Career Profile is the canonical source of user career information.
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AI may also use information explicitly supplied in the current workflow, such as:
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- a job description,
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- user-entered notes,
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- an uploaded document,
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- a CV-specific override.
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AI must preserve provenance wherever practical.
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## Example
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Bad:
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`Added Kubernetes experience because the job requires it.`
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Good:
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`Kubernetes appears in the job requirements, but it is not present in your Career Profile. I have not added it to your CV.`
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## AI interaction model
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Use a hybrid model:
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1. Contextual actions beside content.
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2. A persistent or easily opened contextual assistant.
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3. Command-palette AI actions where useful.
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Avoid creating a disconnected AI area that forces users to leave their workflow.
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## Contextual awareness
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The AI assistant should understand what the user is currently working on.
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Examples:
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### On a Job page
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- How good a match am I?
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- What requirements am I missing?
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- What should I emphasise on my CV?
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- Prepare interview questions.
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- Help me research this role/company.
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- Draft a follow-up message.
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### In a CV
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- Make this shorter.
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- Make this more natural.
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- Tailor this to the selected job.
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- Check for unsupported claims.
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- Improve readability.
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### In Career Profile
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- Suggest missing details to add.
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- Improve wording without changing facts.
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- Identify duplicate information.
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## Review before apply
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AI-generated modifications to user-owned content must not be applied silently.
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Show a preview or diff before committing changes.
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Minimum actions:
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- Accept
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- Reject
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Where practical:
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- Accept individual change
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- Accept all
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- Edit before applying
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## Evidence
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For match analysis, AI should make it clear which Career Profile item supports a claim.
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Example:
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`Strong match: C# / .NET`
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Evidence:
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`Work history → Analyst Programmer → C#/.NET backend development`
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## Match scoring
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If a numeric match score is shown:
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- explain major contributors,
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- distinguish required vs preferred criteria where possible,
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- do not imply mathematical precision beyond the underlying method,
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- do not penalise unknown information as though it were definitely absent unless the product logic explicitly chooses to do so.
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## User control
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Users should be able to:
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- disable or ignore suggestions,
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- review AI output,
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- edit output,
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- understand whether content was AI-assisted.
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## Tone
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AI should be:
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- professional,
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- supportive,
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- concise,
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- practical,
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- non-judgmental.
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Avoid exaggerated motivational language.
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## Privacy and logging
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AI-related data handling should be explicitly documented during implementation.
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Do not assume that all user content may be retained indefinitely.
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Any future analytics about AI usage should prefer aggregate product metrics and avoid exposing private career content to administrators unless required and authorised.
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