Yonatan Steiner

25 posts

How do teams identify failure cases in production LLM systems?

Production LLM systems fail in ways that traditional software never did.Here at PromptLayer, we see firsthand how teams struggle to catch issues that are non-deterministic, context-dependent, and often invisible until a user complains. Unlike a crashed server or a null pointer exception, an LLM failure might look perfectly fluent

How large organizations and enetrrpises standardize LLM benchmarks

As LLMs move from experimental projects into production systems handling real customer queries, financial decisions, and content generation, large organizations face a pressing question: how do you actually evaluate these models in a way that's consistent, comparable, and meaningful? Here at PromptLayer we've watched this challenge

Moltbot Review (formerly Clawdbot)

The idea of a proactive digital assistant has floated around tech circles for years. We’ve watched Siri handle timers and weather queries since 2011, and we’ve chatted with GPT-based tools that forget us the moment we close the tab. At PromptLayer, where we spend a lot of time

How to use an AI agent to sort emails

The relentless flood of emails that fills our inboxes has become an everyday struggle for many, overshadowing the benefits of instant communication. Traditional methods of manually sorting through emails - creating folders or setting rules - often fall short due to the sheer volume and variety. However, the landscape is

AI contextual governance business evolution adaptation

AI is moving from “nice-to-have automation” to a core operating layer inside modern organizations - and that shift forces a rethink of governance. The same rules cannot sensibly apply to a low-stakes customer chatbot and a high-stakes autonomous system, which is why contextual governance is becoming the practical path forward:

Browser-tools-mcp and other methods for agentic browser use

The trajectory of AI has shifted from static text generation to dynamic, agentic execution. In this new paradigm, the web browser represents one of the most important interfaces for AI agents - serving as the gateway to information, SaaS applications, and digital workflows. But integrating large language models with browser

AI contextual refinement

Understanding AI contextual refinement has become essential as technology shifts from focusing solely on prompt engineering to embracing context engineering. As AI models advance, the skill of contextual refinement becomes crucial to optimize outputs, increase accuracy, and reduce hallucinations. Effective context management in LLMs is now critical for higher efficiency

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