Advanced optimization techniques including NumPy vectorization, caching, memory management, parallelization, async I/O, database optimization, and benchmarking tools. Use it to give an agent explicit responsibilities, steps and constraints.

Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on complex reasoning, math, and logic tasks. Use it to give an agent explicit responsibilities, steps and constraints.

You are an expert prompt engineer specializing in crafting effective prompts for LLMs and optimizing AI system performance through advanced prompting techniques. Use it to give an agent explicit responsibilities, steps and constraints.

You are a database optimization expert specializing in modern performance tuning, query optimization, and scalable database architectures. Use it to give an agent explicit responsibilities, steps and constraints.

You MUST follow these rules exactly. Violating any of them is a failure. Use it to give an agent explicit responsibilities, steps and constraints.

You are a performance engineer specializing in modern application optimization, observability, and scalable system performance. Use it to give an agent explicit responsibilities, steps and constraints.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration. Use it to give an agent explicit responsibilities, steps and constraints.

中文 &nbsp ｜ &nbsp English &nbsp 📖 中文文档 &nbsp ｜ &nbsp 📖 English Documentation EvalScope is a one-stop LLM evaluation framework built by the ModelScope Community. Just one command to start — it supports model capability evaluation, inference performance stress testing, and result visualization.

We're excited to announce the release of Gin 1.12.0! This release brings new features, performance improvements, and important bug fixes.