Yonatan Steiner

27 posts

Understanding Intermittent Failures in LLMs

You shipped your LLM application, it passed all your tests, and users loved it. Then, seemingly at random, it starts returning nonsense, timing out, or refusing to answer. The PromptLayer team spends a lot of time observing production behavior in AI applications, and we encounter this question constantly. Intermittent LLM

Opus 4.6 - PromptLayer Team Review

Claude Opus 4.6 landed in February 2026, and the AI community has been buzzing about whether it lives up to the hype. Writing from the PromptLayer team, we've spent considerable time testing this release across coding workflows, long-document analysis, and agentic pipelines. The verdict? Opus 4.6

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:

The first platform built for prompt engineering

Start for free
Made in NYC 🗽
Usage
Company
Follow Us

© Copyright 2026 Magniv, Inc. All rights reserved.