Technical Review

The Technical Review (TR) is your structured career database — the raw material the AI uses to produce tailored CVs. It works like an enhanced LinkedIn experience section: structured, searchable, and AI-ready.

Core Components

Each work experience entry can include:

• Projects with quantified impact metrics
• STAR stories (behavioral examples)
• Emotional context (manager style, environment, stress levels)

Experience Fields

Required Fields

Field	Description
Company name	Current or past employer
Job title	Your role at the company
Start date	Month and year
End date	Month and year (or "Present")

Optional Fields

• Company description
• Location
• Summary narrative
• Skill tags

Projects and Impact

Impact metrics are the single most powerful input for CV generation.

Use specific, quantified measurements:

• ✓ "Reduced API latency by 40% by rewriting the cache layer"
• ✓ "Cut infrastructure costs by $12K/month through rightsizing"
• ✗ "Improved performance" (too vague)

STAR Stories

Behavioral narratives follow a four-part framework:

Part	Description
Situation	Context for the challenge
Task	What you were assigned
Action	Specific steps you took
Result	Quantified outcome

STAR stories are tagged with categories such as: leadership, delivery under pressure, mentoring, conflict resolution.

Completeness Criteria

An experience reaches full completion with:

• At least one project
• At least one STAR story
• Summary text
• Three tagged skills

Incomplete experiences still contribute to CV generation — complete is just better.

Best Practices

1. Add multiple experiences for richer context
2. Use specific technical tags (e.g., "Kubernetes" not "DevOps")
3. Write narratives in first person
4. Save regularly — each save creates a version snapshot you can roll back to

Version History

Your Technical Review is versioned like code. Every time you save, a snapshot is created. You can roll back to any previous version at any time.

Next step: Create an application →