Outlier AI
GenAI Model Evaluation Specialist
Fine-tuning cutting-edge AI models through strategic prompt engineering and comprehensive evaluation
Project Overview
As a GenAI Model Evaluation Specialist at Outlier AI, I led comprehensive testing and fine-tuning of state-of-the-art generative AI models across text, image, and video modalities. My role focused on developing sophisticated prompting strategies to evaluate model performance, identify limitations, and enhance context understanding capabilities for enterprise applications.
Working with multiple client projects, I implemented Retrieval-Augmented Generation (RAG) techniques to improve model accuracy and contextual relevance. By creating meticulously structured prompts and developing comprehensive evaluation frameworks, I helped optimize AI systems for production environments, resulting in significant improvements in model performance metrics and client satisfaction.
My Role
- •Prompt Engineering & Optimization
- •Multimodal Model Evaluation
- •RAG Implementation
- •Performance Metrics Analysis
- •Training Data Curation
Technologies Used
Key Challenges
Complex Prompt Engineering
Developing sophisticated prompts that effectively evaluated model capabilities while maintaining word count constraints. Each prompt needed to include formatting instructions, complexity parameters, and specific evaluation criteria to accurately assess model performance.
Multimodal Data Curation
Sourcing and curating high-quality, diverse datasets across text, image, and video modalities to train and evaluate models. This required extensive research to find relevant content that represented real-world use cases and edge cases for thorough model testing.
Context Understanding Optimization
Enhancing models' ability to comprehend and maintain context across complex, multi-turn interactions. This required developing specialized evaluation frameworks to identify context retention issues and implementing targeted improvements through RAG techniques.
Client-Specific Requirements
Adapting evaluation methodologies to meet diverse client needs across industries, each with unique domain-specific language and use cases. This required rapidly developing expertise in various domains and translating technical AI capabilities into business value.
My Approach
1. Prompt Engineering Framework
I developed a structured framework for creating effective prompts that systematically evaluated model capabilities:
- •Defined clear evaluation criteria and metrics
- •Created templates with formatting instructions
- •Incorporated complexity parameters
- •Designed multi-turn conversation scenarios
- •Optimized for word count constraints
2. Multimodal Data Curation
I implemented a comprehensive approach to source and curate high-quality training data:
- •Developed data quality assessment criteria
- •Created diverse datasets across modalities
- •Ensured representation of edge cases
- •Implemented data augmentation techniques
- •Maintained metadata for traceability
3. RAG Implementation
I leveraged Retrieval-Augmented Generation to enhance model performance:
- •Configured vector databases for efficient retrieval
- •Optimized embedding models for semantic search
- •Implemented context window management
- •Developed hybrid retrieval strategies
- •Created feedback loops for continuous improvement
Key Achievements
Improved Response Quality
Enhanced model response quality across all evaluated metrics through strategic prompt engineering and RAG implementation.
Client Projects
Worked with clients requiring text, image, and video prompts across various fields including coding, mathematics, science, literature, and art.
Prompts Created
Crafted over 500 specialized prompts spanning diverse domains, complexity levels, and technical requirements, consistently delivering high-quality results.