ClinoveraThe rapid advancement of generative AI has highlighted the critical need for rigorous evaluation that extends beyond initial deployment. Unlike traditional software, GenAI models can produce unpredictable, harmful, or biased outputs if not carefully designed and tested. A multi-faceted approach to evaluation is essential to identify, measure, and mitigate these risks, ensuring continued customer satisfaction and reliable support.
Gen AI Evaluation Services
We use GenAI tools to accelerate development therefore reducing costs, improving efficiency, and ensuring consistent high-quality software.
Services
GenAI Evaluation and Quality Assurance
A comprehensive assessment of GenAI models to ensure accuracy, reliability, and safety. Includes the identification and mitigation of biases, hallucinations, and the development and application of tailored evaluation metrics.v
Risk Management
We proactively identify and assess the risks associated with GenAI systems. Includes the development and implementation of risk mitigation strategies, and ongoing monitoring and response to emerging risks.
Optimization and Improvement
The continuous evaluation of GenAI models to identify performance bottlenecks, and the implementation of optimization techniques to enhance model accuracy and efficiency.
Dataset Development and Management
The creation and curation of high-quality datasets for training and evaluating GenAI models. Includes ata cleaning, labeling, augmentation to improve model performance, and data governance and privacy compliance.
Tooling and Infrastructure
The selection, implementation, and optimization of AI tools and platforms. We handle design and deployment of scalable AI infrastructure as well as the integration of AI into operations for efficient workflows.
AI Strategy and Consulting
The development of tailored GenAI strategies aligned with business objectives.
Business Challenges
Unpredictable Outputs
Generative AI produces content that is often unpredictable, making it challenging to achieve consistency and reliability.
Exposure of Sensitive Information
These models can inadvertently reveal personal or sensitive data from their training sets, creating privacy concerns.
Fabricated Information
Generative AI can create realistic but entirely fictional content, a problem that cannot be easily solved with simple configuration adjustments.
Bias Inclusion
The datasets used for training may contain biases, such as political views or discriminatory language, which can then appear in the AI-generated outputs
Outdated Data
Over time, the information used to train AI models can become outdated. Updating these models with new data is expensive and technically demanding.
Manipulated Outputs
Hackers can exploit AI models to generate misleading or entirely false information, complicating the detection and correction of these malicious manipulations.
Latency Issues
The time it takes to generate outputs can impact on user retention, affecting the user experience of the AI solution.
How we work
During the evaluation process, our AI QA Engineers employ various methods to evaluate Gen AI solutions:
- Utilizing Human-Created Datasets: leverage datasets created by humans that encompass specific domain knowledge, biases, security aspects, and more to evaluate different facets of Gen AI-based solutions.
- Employing LLM Models with Human review: use Large Language Models (LLMs) to generate datasets, which are then used to thoroughly evaluate Gen AI applications under supervision of an AI expert.
Depending on requirements our team will be able to evaluate Gen AI solution using various types of metrics:
- Quality Metrics: Use AI-assisted metrics like Correctness, Coherence, Completeness, Relevance, Similarity, Context Adherence, Refusals and others to ensure high-quality AI outputs.
- Risk & Safety Metrics: Check for risks like Jailbreak defects, Hallucinations, Toxicity and Biases, ensuring safe and ethical AI use.
- Technical Metrics: Depends on type of AI-Based solution additionally can be evaluated F1 Score, N-Gram metrics (BLEU, ROUGE), Semantic Metrics (BERTScore, BLEURT, etc.), Image metrics (PSNR, SSIM)
- Custom Metrics: Create metrics tailored to client needs, offering flexibility and precision in our testing.
Our Tools
We are official provider of Microsoft, that is why we are primary using Microsoft Azure AI studio and other Microsoft evaluation frameworks such as: Microsoft Prompt Shields, Microsoft PyRIT, Azure AI Content Safety and others.
But if we are not using Azure AI, we can built and provide evaluation using other solutions: DeepEval, LangKit, Google Vertex AI, Gallileo LLM Studio.
“GenAI has revolutionized software development, offering endless possibilities for engineers. This shift challenges traditional quality assurance methods, but pioneering work in data science and cybersecurity has led to new approaches. We’re thrilled to introduce GenAI Evaluation, a vital part of this new frontier, aimed at equipping individuals with the knowledge needed to navigate this exciting technology”
Pavel Khodalev, SVP
Area Leader
Alexander Meshkov 
QA Delivery Director, First Line Software
Alexander Meshkov is QA Delivery Director at First Line Software. Alexander has over 10 years of experience in software testing, organization of the testing process, and test management. A frequent attendee and speaker of diverse testing conferences, actively engages in discussions and keeps up-to-date with the latest trends and advancements in the field.
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First Line Software is a premier provider of software engineering, software enablement, and digital transformation services. Headquartered in Cambridge, Massachusetts, the global staff of 450 technical experts serve clients across North America, Europe, Asia, and Australia.