Evaluating GenAI Solutions: What You Need to Know

Over the past two years, there has been a significant interest in adopting generative AI tools in the legal industry. However, many organizations continue to face challenges in understanding how to procure, implement, and maximize the potential of these tools. This article seeks to address this gap by offering key insights and questions to consider when assessing GenAI solutions.

Key areas covered include:

1. Foundational Models: Understanding the underlying large language models (LLMs) used by providers is essential. Different models have varying strengths and weaknesses, and continuous evaluation is necessary to ensure optimal performance.

2. Data Security: Ensuring that providers have robust security processes and certifications is vital. Questions about data storage, access controls, and third-party service providers should be addressed to maintain data integrity and security.

3. Accuracy and Reliability: Evaluating the accuracy of GenAI tools is critical. Providers should provide benchmarks, case studies, and details on how they handle model drift and degradation. High accuracy translates to higher reliability and consistent performance.

   
   

By addressing these key areas, organizations can make informed decisions and successfully integrate GenAI tools into their operation.

When selecting a GenAI solution, the first step is having a clearly defined use case. AI models vary in their capabilities, strengths, and weaknesses, so understanding what you need the AI to accomplish ensures that you evaluate solutions effectively and choose one that aligns with your business goals.

Foundational Models.

The foundational model of a Large Language Model (LLM) is critical to get right because it serves as the core engine that determines the capabilities, limitations, and overall effectiveness of a generative AI solution. Below are listed some key questions you should ask GenAI legal tech providers when evaluating potential options. Check if you’ll be locked in by the provider’s choice of model. This restricts your flexibility to swap between different foundational models should a better performing one or one that matches your needs more closely emerges.

What foundational model does the provider use?

Understand the underlying large language model(s) that provides insights into the tool’s capabilities and potential limitations. Different foundational models such as OpenAI’s GPT, Google’s Gemini, Meta’s Llama, and Anthropic’s Claude vary in terms of architecture, training data, and optimization strategies. The choice of model impacts accuracy, fluency, bias, contextual awareness, and multimodal capabilities (e.g., handling text, images, and code).

How do they ensure they are using the best performing LLM?

The landscape of LLMs is rapidly evolving. Providers should have a robust process for continuous model evaluation to ensure they are leveraging the most effective model available. A strong evaluation framework should incorporate standardized performance benchmarks like MMLU (Massive Multitask Language Understanding), SuperGLUE, and HELM (Holistic Evaluation of Language Models) to measure the model’s accuracy, reasoning ability, and bias levels. Additionally, providers should conduct domain-specific testing if the AI is being used in industries like healthcare, finance or legal applications, ensuring the model meets the necessary precision and any relevant compliance standards.

How often are the models updated and retrained?

Regular updates and retraining are crucial for maintaining a LLM that remains accurate, relevant, and aligned with evolving knowledge base and end user needs. Models can quickly become outdated as new facts, regulations, and industry trends emerge, making it essential for providers to have a structured retraining and updating cycle. Buyers should inquire about the frequency and methodology of these updates to ensure the model is continuously improving. Some providers update their models on a fixed schedule, such as quarterly or annually, while others use a rolling update approach, where models are incrementally retrained with new data as it becomes available.

Can you inject your own data into the pre-existing LLM to fine-tune the results?Customization may be necessary to align the LLM with specific business needs, industry requirements, or proprietary knowledge. The ability to fine-tune a pre-existing LLM using your own data can significantly enhance its relevance, accuracy, and effectiveness for specialized applications. Organizations should assess whether the provider supports fine-tuning, embedding domain-specific knowledge, or integrating external databases to tailor responses.

Data security. While the GenAI space is moving quickly, providers need to ensure that they're keeping your data secure at all times and they have the correct protocols in place to deal with any potential breaches. As part of your evaluation, you'll need to be satisfied that the provider has the right security processes and certifications in place. Depending on your use case and the solution being considered, the key questions to ask the provider are listed below.

Does the provider have security certifications?Security certifications are a crucial indicator of an AI provider’s commitment to data protection, data privacy, and compliance with industry standards. Buyers should look for recognized security frameworks such as ISO 27001, which ensures a robust information security management system, or SOC 2 (Service Organization Control 2), which evaluates how well a provider safeguards customer data in terms of security, availability, processing integrity, confidentiality, and privacy. Some startups might not have the right certifications in place. In that case, request penetration test results and ask how often testing, both internal and external, is carried out.

Where will your data be stored or hosted?Understanding where your data is stored and processed is critical for ensuring compliance with data residency, security, and regulatory requirements. Buyers should verify whether the AI provider offers flexible hosting options, such as on-premise deployment, private cloud, hybrid cloud, or specific regional data centers, to align with their internal policies and legal obligations.

What are the access controls and authentication options?Robust access controls and authentication mechanisms are essential to ensure that only authorized personnel can interact with AI systems, particularly when dealing with sensitive data, proprietary knowledge, or regulated industries. Buyers should evaluate whether the provider offers Role-Based Access Control (RBAC), which allows administrators to restrict access based on job function, seniority, department, or geographic location. For example, executives may have full system access, while frontline employees may have read-only permissions, and IT administrators may have advanced configuration rights.Does the provider rely on third party service providers to deliver their service?In most cases, AI providers rely on third-party service providers for various aspects of their infrastructure, including cloud hosting, data storage, API integrations, and security. It’s important to understand who these third parties are, what role they play, and how they handle your data to ensure compliance with security and privacy requirements. Additionally, businesses should clarify if any subcontractors have access to sensitive or proprietary information and what measures are in place to prevent data misuse.

Accuracy and Reliability

When evaluating generative ai tools, understanding the accuracy of the model is crucial. The quality of the output is directly dependant on the accuracy of the model. High accuracy translates to higher reliability. Reliability means the solution consistently provides accurate and dependable results across various scenarios and over time.

What metrics do you use to measure the accuracy of your models?When evaluating an AI provider, it’s crucial to understand how they measure model accuracy and which metrics they prioritize in relation to your specific use case. Common benchmarks include Perplexity (PPL) for predictive accuracy, BLEU and ROUGE for translation and summarization, Exact Match (EM) and F1 Score for classification and retrieval tasks, and TruthfulQA/FEVER for factual accuracy. Note that most benchmarks have some limitations. Ask about false positive rates and whether accuracy can be fine-tuned for industry-specific needs. Additionally, assess if and how the provider monitors real-world performance through human-in-the-loop validation, A/B testing, and live feedback loops to ensure ongoing improvements.  

What processes are in place to monitor and maintain the model's accuracy over time? Over time, LLMs can experience model drift and degradation, where their responses become less accurate, biased, or misaligned with current data trends. This happens because language evolves, facts change, and business needs shift. To ensure long-term reliability, ask the provider what monitoring and maintenance strategies they use to track, evaluate, and update the model’s performance. Without proper monitoring and maintenance, AI models can become outdated and unreliable. Provider that implements proactive tracking, continuous fine-tuning, and conduct real-world performance evaluations ensure that the model remains accurate, unbiased, and aligned with evolving business needs.

Can the vendor provide details on the performance of their solution in real-world scenarios?Evaluating an AI provider based on real-world performance is essential to understanding how their solution functions beyond controlled environments and benchmark tests. Ask the provider for case studies, references, and deployment examples that demonstrate how their solution performs in organizations of similar size, industry, and complexity as yours.

How do you evaluate the solution’s performance on new data?This question suggests an educated buyer who is thinking beyond their current use case and where and how to deploy the solution more widely. For businesses looking to scale adoption across multiple use cases, the solution must seamlessly handle evolving datasets without frequent or laborious manual intervention. Providers with robust evaluation strategies, automated monitoring, and lightweight adaptation options ensure that the AI remains accurate, adaptable, and future proof, reducing the need for constant retraining while continually maintaining high performance.

Generative AI tools offer immense potential for organizations ready to harness their power. By clearly defining use cases, understanding foundational models, ensuring robust data security, and evaluating accuracy and reliability, businesses can make smart, informed decisions. Staying proactive and informed will be key to leveraging these advanced technologies effectively and avoiding the dreaded Shiny New Toy Syndrome.

Sharan Kaur – Go-To-Market (GTM) Expert | Legal Tech Strategist | Growth Leader

Sharan Kaur is a seasoned growth and sales leader with a proven track record of designing and executing global go-to-market (GTM) strategies for startups, scaleups, and legal tech providers. With a background as a corporate litigation lawyer and an Executive MBA, Sharan specializes in driving revenue growth, leading high-performance teams, and implementing scalable solutions for long-term success.

Her expertise lies in managing full sales cycles, building strategic partnerships, and consulting post-deployment to ensure maximum value realization. Sharan works closely with law firms, corporate legal teams, and legal tech providers to deliver custom solutions, optimize workflows, and enhance user adoption of innovative technologies.

Currently, as a Digital Transformation Consultant at Legal Solutions Consulting, Sharan bridges the gap between legal teams and generative AI solutions, ensuring seamless adoption and long-term value realization. Her cross-functional leadership experience and deep understanding of legal technology adoption make her a trusted advisor for businesses seeking sustainable growth and operational excellence.