Evaluation tools in critical operations of AI solutions

Learn why AI evaluation tools are crucial for the safe and effective operation of AI solutions in organizations.

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As organizations increasingly depend on generative AI applications, ensuring these solutions’ quality, correctness, and security becomes essential. AI evaluation tools have become vital in critical operations of AI systems, especially in managing Governance, Risk, and Compliance (GRC). Organizations must rethink GRC in generative AI, where results are probabilistic.

Evaluation tools enable organizations to monitor and continuously assess AI-driven processes while identifying hidden risks. This emerging non-deterministic nature of generative AI requires innovative approaches to monitoring and managing GRC that differ from traditional, deterministic software systems and security tools.

All Things Open AI workshops and conference March 17-18, 2025

Why AI evaluation tools are essential

Enhancing decision-making and risk management

AI evaluation tools leverage machine learning, natural language processing, and predictive analytics to process vast amounts of structured and unstructured data. This enables them to:

• Provide production monitoring: Tools like Arize, Galileo, and Langsmith continuously track and evaluate AI model performance in production, monitoring key metrics, data drift, and potential biases.
  
• Debug and optimize: They provide analysis and model debugging tools, enabling proactive interventions and continuous improvement of AI solutions.
  
• Validate pre-production pipelines: Evaluation tools can provide a Test-Driven Development (TDD) implementation that simulates and assesses the Chatbot and AI service workflow, from information retrieval and prompt engineering to completion. This ensures that each stage is tuned, enabling an easier deployment and robust performance once in production.

These benefits help ensure that AI systems function within established governance frameworks and provide results aligning with organizational and regulatory standards.

Streamlining compliance and ethical governance

AI evaluation tools can automate the mapping of evolving regulations and continuously monitor compliance efforts. This is particularly significant in sectors where compliance errors can have severe financial and reputational consequences (i.e., protecting the brand).

Furthermore, by integrating explainable AI (XAI) capabilities, these tools provide human-readable descriptions for their outputs. This accountability builds trust and facilitates a better understanding of decisions—a key requirement for responsible governance in an AI ecosystem.

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How and why evaluations work

Modern AI model observability and evaluation platforms are essential for developing, deploying, and maintaining large language models, architectures, and AI services. This is how they typically work.

These platforms can continuously monitor AI services in development, QA, and production, tracking key metrics like response quality, latency, token usage, and cost. They create detailed traces of each model inference, recording the entire chain of operations from initial prompt to final output, which helps developers identify problems, latency, and errors.

For development, they offer systematic, prompt engineering environments where teams can test ground truth, compare results, and refine their methods based on quantitative and qualitative analysis. They maintain evaluation datasets that are standardized benchmarks for assessing model performance over time. Many organizations have integrated evaluation tools into their continuous delivery platforms (e.g., within their DevOps, DevSecOps, and SRE practices). 

Most evaluation platforms include feedback collection mechanisms, enabling automated evaluation against defined metrics and human evaluation integration. The data gathered flows into dashboards and alerting systems that visualize performance trends and notify stakeholders of anomalies or loss of performance.

The principal benefit involves transforming “black box” AI systems into transparent, measurable processes. Black box AI systems are models whose internal details are hidden, making it difficult to interpret how they transform inputs into outputs. By providing visibility into every step of model execution (i.e., retrieval, prompt, completion, results) and collecting structured performance data, these platforms enable continuous improvement cycles where insights from production directly inform development improvements.

Read more: GenAI’s promise and peril: Tools, risks, and opportunities

The nondeterministic nature of generative AI and new GRC monitoring needs

Unlike traditional software development—where deterministic processes ensure that the same input always produces the same output—generative AI is, by definition, non-deterministic. This means:

• Variable outputs: Generative AI models produce outputs based on probabilistic algorithms, which can lead to variations even with the same inputs. This unpredictability challenges the validation and monitoring processes traditionally applied to deterministic software.
• Dynamic risk profiles: Because generative AI can change its behavior over time (for example, as it learns from new data or is fine-tuned), its risk level is not static. Traditional GRC frameworks, which rely on fixed rules and routine audits, may fail to capture these dynamic shifts.
  • For instance, in conventional test-driven development (TDD), we typically focus our tests on changes to the software rather than the data. We must employ fully automated integration with models to test our software changes, which include any changes in data inputs for retrieval, adjustments to model parameters like weights, updated training data, and the algorithms that yield the results.
• Need for continuous monitoring: Generative AI’s nondeterministic nature requires organizations to adopt continuous and adaptive monitoring systems. AI evaluation tools must be able to track changes in model behavior in real-time, adjust risk scores dynamically, and provide ongoing insights into compliance and operational integrity.

By incorporating these tools into their GRC processes, organizations can establish that even as AI solutions generate varied outputs, any deviations or unexpected behaviors are quickly identified and addressed.

Adobe at ETLS

Brian Scott at Adobe gave a fantastic presentation at Gene Kim’s Enterprise Technology Leadership Summit in September 2024 titled Generative AI Governance at Scale. A key focus of this presentation was the rethinking process required to shift from a state of chaos to an efficient, effective AI governance model.

This involves a multi-step approach: First, slowing down processes to perform thorough reviews and refinements; then simplifying operations by consolidating entry points and standardizing use case triage; and finally, elevating high-value applications while ensuring minimal risk. This method is reinforced through risk-scoring systems that evaluate security, legal, and privacy factors, providing clear metrics to guide decision-making and resource allocation.

Additionally, Brian emphasized the importance of developing a scalable and adaptive generative AI strategy. Adobe’s approach includes managing model repositories, leveraging hackathons and concepts for innovation, and incorporating best practices from across the industry. By focusing on transparency, early warning systems, and continuous process improvements, Adobe is determined to maintain robust oversight over AI operations, helping to ensure that generative AI deployment remains innovative and compliant with evolving regulatory demands.

Conclusion

AI evaluation tools are crucial for the safe and effective operation of AI solutions in organizations. They provide continuous, real-time insights into risk, compliance, and ethical governance, ensuring that even the unpredictable outputs of generative AI are controlled within a robust GRC framework. By identifying hidden patterns, offering explainable observations, and adapting to evolving threats, these tools help organizations overcome the complexities of a rapidly changing technological landscape.

Implementing advanced evaluation tools is crucial for maintaining operational resilience and regulatory compliance in organizations where the outputs of generative AI have consequences. For more information about the operation of AI services at scale, please check out my DearCIO newsletter.

All Things Open AI workshops and conference March 17-18, 2025

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