Yonatan Steiner

A deep dive into LLM observability tools

You ship a feature powered by a language model, and for three weeks everything works beautifully. Then support tickets start trickling in - users reporting confident-sounding answers that are completely wrong. You check the logs, but all you see are successful API responses. The model returned text, so technically nothing

Best prompt management platforms: Features, comparisons, and recommendations

The shift from experimental prompting to production-grade AI systems has created a genuine infrastructure gap. Teams that started with a few prompts in a text file now find themselves managing hundreds of variations across models, environments, and use cases - with no clear way to track what changed, why, or

Multi-agent collaboration via evolving orchestration

A NeurIPS 2025 paper introduces dynamic orchestration where a central "puppeteer" learns to route tasks between agents based on evolving problem states, outperforming fixed multi-agent pipelines.

Prompt Repetition Improves Non-Reasoning LLMs: Google's New Study

Google researchers found that simply repeating your prompt—copying and pasting it twice—dramatically improves LLM accuracy on non-reasoning tasks, with gains up to 76% and zero performance degradation.

Benchmarking Gemini 3.1 Pro: Latency, cost, and reasoning trade-offs

Google's Gemini 3.1 Pro represents a meaningful step forward for developers building applications that require advanced reasoning. Announced in February 2026, the model promises smarter problem-solving without forcing users to pay more for the privilege. At PromptLayer, where teams manage prompts and evaluate model performance, we'

How do you observe LLM systems in production?

Deploying LLMs is only half the battle — once live, they can hallucinate, drain budgets, or slow down in ways standard monitoring never catches. LLM observability connects inputs, outputs, latency, cost, and quality into a single picture.

Why LLM Evaluation Results Aren't Reproducible (And What to Do About It)

Ever run the same AI model twice and gotten different answers? You're not imagining things. The PromptLayer team have seen this frustration play out repeatedly across research labs and production systems alike. Reproducibility - the ability to achieve consistent results under the same conditions - is foundational to

Super Claude Code: How structured prompts turn Claude Code into a true development partner

AI coding assistants have become genuinely useful, but getting consistent, expert-level output from them remains surprisingly tricky. Developers struggle with the gap between an LLM's raw potential and its actual performance on complex coding tasks. SuperClaude, a community-built framework created by developer Anton Knorery, addresses this challenge head-on

Claude-opus-4-1-20250805-thinking-16k: What the Thinking-16k label actually means for your workflows

Claude Opus 4.1 arrived on August 5, 2025, and with it came a naming convention that caused some confusion. claude-opus-4-1-20250805-thinking-16k - is this a separate model, a configuration, or something else entirely? The short answer: it is a specific reasoning budget configuration of Anthropic's flagship model, and

A deep dive into LLM observability tools

Best prompt management platforms: Features, comparisons, and recommendations

Multi-agent collaboration via evolving orchestration

Prompt Repetition Improves Non-Reasoning LLMs: Google's New Study

Benchmarking Gemini 3.1 Pro: Latency, cost, and reasoning trade-offs

How do you observe LLM systems in production?

Why LLM Evaluation Results Aren't Reproducible (And What to Do About It)

Super Claude Code: How structured prompts turn Claude Code into a true development partner

Claude-opus-4-1-20250805-thinking-16k: What the Thinking-16k label actually means for your workflows

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