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WHY ENTERPRISE AI ALSO NEEDS OBSERVABILITY TOOLS
We need observability in AI because this technology is starting to show limitations while becoming indispensable, and for enterprises, these limitations are unacceptable. Observability will help African companies evaluate and monitor, quality of inputs, outputs, results of LLM-based applications and can flag hallucinations, bias, toxicity, performance and cost issues, says Anupam Datta at Snowflake.
The use of AI-powered applications, from virtual assistants and chatbots to coding copilots and autonomous agents, is gaining popularity among businesses. However, as their adoption grows, their flaws are becoming increasingly apparent.
Issues such as incomplete, offensive or inaccurate responses, often referred to as hallucinations, security risks, and overly generic replies present barriers to enterprise-wide implementation, and for good reason.
LLMs are trained to generalise from large bodies of text, generating original text modelled on general patterns found in the text, they are not built to memorise facts.
Similar to how the rise of cloud-based platforms and applications introduced several innovative tools used to assess, debug, and monitor their functionality, the widespread adoption of AI demands a dedicated suite of observability tools tailored to its own unique requirements.
Observability refers to the technologies and business practices used to understand the complete state of a technical system, platform, or application. For AI-powered applications specifically, observability means understanding all aspects of the system, from end to end.
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