Princess

Key Skills

Software

Don't disclose

Top Subject Matter

AI training image datasets and visual question/answer style quality review

Prompt evaluation and AI-assisted review for visual QA outputs

Legal Services & Contract Review

Top Data Types

Image

Text

Document

Top Task Types

Prompt + Response Writing (SFT)

Freelancer Overview

Freelancer - AI Training Data, Visual QA and Documentation | Independent / Remote. Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Don't disclose, ChatGPT, and Claude. Education includes Bachelor of Technology, Indira Gandhi Delhi Technical University for Women (2025). AI-training focus includes data types such as Image and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

Freelancer - AI Training Data, Visual QA and Documentation | Independent / Remote (prompt/policy evaluation aspects)

ImagePrompt + Response Writing (SFT)

Conducted prompt evaluation thinking while manually reviewing final AI outputs for accuracy, usability, and adherence to dataset instructions. Supported research-driven prompt refinement and categorization ideas using AI-assisted draft review cycles. Focused on human-in-the-loop verification to validate that outputs matched intended user intent and task guidance. • Iteratively refined prompts and assessed whether responses aligned with labeling/checklist criteria • Verified consistency of outputs with expected behavior and dataset instructions • Performed manual final checks after AI-assisted research and draft generation • Documented review reasoning as part of quality-review and submission readiness

2025 - Present

Freelancer - AI Training Data, Visual QA and Documentation | Independent / Remote

Don't discloseImage

Performed AI training visual QA by reviewing image datasets for clarity, lighting, object visibility, background suitability, and blur/noise quality. Built and applied instruction-following review workflows to ensure requirements, naming accuracy, and submission readiness were met. Produced metadata-style notes and structured dataset organization to make outputs auditable for model learning use cases. • Reviewed base-scene, isolated-object, and integrated final-scene image outputs against visual expectations • Checked consistency across camera/scene conditions and indoor/outdoor diversity • Generated quality-review notes, folder structures, and naming conventions for dataset usability • Used AI tools to refine prompts and validate categorizations while doing final manual accuracy checks

2025 - Present

Education

Work History