The proliferation of generative AI-powered applications in enterprises has brought to the forefront their shortcomings, including incomplete, offensive, or wildly inaccurate responses, security vulnerabilities, and disappointingly generic responses. To address these issues, AI observability has emerged as a crucial technology, enabling businesses to understand the complete state of their AI systems, flag and diagnose problems, and ensure their applications meet business needs.

AI observability refers to the technologies and business practices used to comprehend all aspects of an AI system, from end to end. This includes evaluating and monitoring the quality of inputs, outputs, and intermediate results of applications based on large language models (LLMs). By doing so, observability helps companies identify and address hallucinations, bias, and toxicity, as well as performance and cost issues.

The limitations of AI technology are becoming increasingly apparent, and for enterprises, these limitations are unacceptable. For instance, large language models are trained to generalize from large bodies of text, generating original text modeled on the general patterns found in the text they were trained on. They are not built to memorize facts. However, when used in place of search engines, users expect accurate and helpful results. If the AI fails to deliver, it erodes trust and can cause damage to the brand.

AI observability provides the power to monitor, measure, and correct performance, helping in three key aspects of corporate AI use: evaluation and experimentation, monitoring and iteration, and tracking costs and latency. With observability, enterprises can easily determine which AI models and tools work best for their specific use case, optimize their tech choices, and diagnose and fix problems.

As AI applications become more critical to business infrastructure, they must meet the "3H rule," being honest, harmless, and helpful. Honest AI means factually accurate and free of hallucinations, harmless AI ensures answers don't leak personally identifiable information and are not vulnerable to attacks, and helpful AI delivers answers that match user queries and provide useful results.

The RAG Triad, a framework for evaluating AI apps, helps ensure that AI applications are honest and helpful. It includes three metrics – context relevance, groundedness, and answer relevance – to measure the quality of the three steps of a typical RAG application. By decomposing a composite RAG system into components, this evaluation framework can triage failure points and provide a clearer understanding of where improvements are needed.

Guarding against harm involves aligning models to guard against safety risks and adding guardrails to applications for metrics related to toxicity, stereotyping, and adversarial attacks. With AI observability, we can guard against hallucinations, catch irrelevant and incomplete responses, and identify security lapses, enabling businesses to harness the full potential of AI to transform their operations, optimize processes, reduce costs, and unlock new revenue.

Anupam Datta, principal research scientist and AI research team lead at Snowflake, emphasizes the importance of AI observability in ensuring the reliability and accuracy of AI applications. As AI technology continues to evolve, the need for observability will only grow, underscoring its critical role in enabling businesses to trust and deploy AI applications with confidence.

In conclusion, AI observability is a crucial technology that enables businesses to ensure the reliability, accuracy, and security of their AI applications. As generative AI applications become increasingly prevalent, the importance of observability will only continue to grow, enabling businesses to harness the full potential of AI to transform their operations and drive innovation.