Yonatan Steiner

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

Is Opus smarter than Sonnet? Opus vs. Sonnet

The question of which AI model is "smarter" depends entirely on what you need that intelligence to do. At PromptLayer, we spend a lot of time watching how different models perform across real workflows. Both models come from Anthropic's Claude family, but they serve fundamentally different

Prompt routers and flow engineering: building modular, self-correcting agent systems

The shift from crafting individual prompts to designing entire reasoning flows has fundamentally changed how we build AI applications. The PromptLayer team have watched this evolution closely, observing how teams move from trial-and-error prompt tweaking toward systematic architectures that can catch their own mistakes. This transition represents more than a

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

Is Opus smarter than Sonnet? Opus vs. Sonnet

Prompt routers and flow engineering: building modular, self-correcting agent systems

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