Artificial intelligence applications are increasingly enticing companies. However, their growth highlights their limitations. Incomplete, offensive, or entirely inaccurate responses (commonly referred to as "hallucinations"), security vulnerabilities, and overly generic responses slow their widespread adoption.
Hallucinations, security flaws, and errors undermine companies' trust in their AI applications and hinder their deployment. As illustrated by the case of two lawyers sanctioned for submitting a legal brief containing fictional cases generated by AI, inappropriate responses from a LLM can tarnish an image and reputation by eroding trust.
Observability encompasses technologies and practices that enable understanding the state of a technical system. For AI applications, this means a complete, end-to-end view. It helps companies assess the quality of language model (LLM) outputs while detecting hallucinations, biases, toxicity, performance issues, and costs. We need observability in AI because this technology is starting to show its limits precisely when it becomes indispensable. When replacing search engines, users of LLMs expect them to provide accurate answers. If AI fails at this task, it erodes trust.
Just as the cloud spawned tools to assess and monitor its services, the rise of artificial intelligence demands its own observability solutions. AI applications can no longer be treated as mere experiments. They must be managed with the same rigor as any critical application.
Going Beyond "This Seems Right"
One of the main challenges for organizations using AI is having a reliable means to assess model accuracy. From evaluation to monitoring, observability plays a key role in managing AI application performance. It allows identifying the most suitable solutions among the diversity of available models and tools, ensuring continuous tracking after deployment to detect and correct potential anomalies, and optimizing the balance between performance, latency, and costs. By integrating these mechanisms, organizations can leverage AI more efficiently and effectively.
What Companies Must Demand from AI
To deploy AI with confidence, companies must aim for a high level of demand, far beyond the simple "good enough." LLM responses must be honest, inoffensive, and useful.
They must rely on verifiable facts, free from errors or inventions, and excel in complex tasks such as summarization, inference, or planning. A responsible AI also knows how to recognize its limits and refrain from responding in the absence of information. Security is paramount; AI must neither expose personal data nor succumb to manipulations. Robust mechanisms must prevent biases, stereotypes, and toxic drift. Finally, artificial intelligence must produce clear, useful, and directly exploitable responses, serving users' goals to enhance their efficiency and decision quality.
For tasks requiring reliable memorization, LLMs need to be enriched with external data sources to ensure accuracy. This is the principle of retrieval-augmented generation (RAG), which combines LLMs and factual databases for more accurate answers.
The RAG Triad is a set of metrics for evaluating RAG applications to ensure they are honest and useful. It is based on three criteria: Context Relevance, Grounding, and Answer Relevance. By breaking down a RAG system into its elements (query, context, response), this evaluation framework helps identify failure points and optimize the system in a targeted manner.
Protecting Against Risks
Observability helps limit hallucinations, detect erroneous responses, and identify security flaws. With the emergence of multi-agent workflows, it becomes crucial to monitor tool calls, execution traces, and the proper functioning of distributed systems. Protecting against risks involves aligning models and adding safeguards to applications to assess toxicity, stereotypes, and adversarial attacks. It's a key technology to fully exploit AI's potential, transform businesses, optimize processes, reduce costs, and unlock new revenue sources.