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D
Daniel E.

Daniel E.

Advanced Data Evaluation & Prompt Optimization — Output Evaluation & Error Identification

Nigeria flagAbia state, Nigeria

Key Skills

Software

No software listed

Top Subject Matter

AI-generated content auditing
business funnel/categorization
RLHF-style output evaluation

Top Data Types

TextText
ImageImage

Top Task Types

RLHFRLHF

Freelancer Overview

Advanced Data Evaluation & Prompt Optimization — Output Evaluation & Error Identification. AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Advanced Data Evaluation & Prompt Optimization — Multimodal Prompt Labeling (Visual Taxonomy)

ImageImageRLHFRLHF

Engineered a structured labeling taxonomy for multimodal visual AI engines to train more consistent, studio-grade cinematic outputs. Labeled and defined visual attributes spanning subject details, lens physics, lighting geometry, and color profiles to support model learning. Applied the taxonomy as part of prompt labeling and output-constraint alignment for improved generation quality. • Created a 5-part taxonomy for visual attribute labeling • Defined labels for subject details, lens physics, lighting geometry, and color profiles • Prepared labeled prompts/tags to train multimodal visual consistency • Supported constraint enforcement via taxonomy-driven prompt labeling

Present

Advanced Data Evaluation & Prompt Optimization — Output Evaluation & Error Identification

TextText

Conducted output evaluation for AI-generated analysis by auditing responses and identifying logical/business-logic flaws in categorization of a funnel bottleneck. Revised labeled data to align model outputs with real-world industry benchmarks and correct the underlying interpretation. Used rigorous labeling guidelines to ensure consistent evaluation behavior across the studied content. • Audited AI-generated data analysis reports for categorization errors • Identified a critical business-logic flaw impacting funnel bottleneck labeling • Corrected/realigned data labels to match industry benchmarks • Established evaluation and labeling guidelines for reliable output assessment

Present

Education

U

University of Uyo, B.A

Degree not specified

Not specified
Not specified

Work History

C

Company not specified

Teacher.

Location not specified
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