Translate the following blog post title into English, concise and natural. Return plain text only without quotes. 谷歌推出 LLM-Evalkit，为提示词工程带来秩序与可衡量性
Google Launches LLM-Evalkit for Structured Prompt Engineering
Date: 2025-10-29 08:24 Beijing
Google has introduced LLM-Evalkit, a new open-source framework designed to bring structure, measurability, and collaboration to prompt engineering for large language models.
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Overview
Built on the Vertex AI SDK, LLM-Evalkit replaces guesswork-driven workflows with data-backed, unified processes. It allows teams to:
- Create, test, version, and compare prompts side-by-side
- Maintain a centralized, shared record of prompt iterations
- Apply consistent evaluation methods across experiments
Michael Santoro summed up the pain points the tool addresses: teams previously bounced between consoles, stored prompts in multiple locations, and lacked a standard framework for measuring improvements.
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Key Features
1. Stop Guessing, Start Measuring
- Define concrete tasks
- Prepare representative datasets
- Evaluate outputs using objective metrics
- Shift from intuition-based to evidence-based improvements
2. Tight Google Cloud Integration
- Directly connects with Vertex AI SDK
- Links to Google Cloud’s professional evaluation tools
- Maintains a single source of truth for all prompt iterations
- No need to juggle multiple environments
3. Lower Barriers for All Roles
- Includes a no-code interface
- Accessible to developers, data scientists, product managers, UX writers, and more
- Encourages collaboration between technical and non-technical members
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Community Reaction
Santoro on LinkedIn:
> I’m honored to announce that I contributed to developing a brand-new open-source framework—LLM-Evalkit! It’s designed to simplify the prompt engineering process for teams using large language models on Google Cloud.
A LinkedIn user commented:
> This looks fantastic. We’ve long struggled without a centralized system to track prompts, especially as models keep evolving. I can’t wait to try it out.
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Availability
- Open-source project now live on GitHub
- Fully integrated with Vertex AI
- Includes detailed tutorials in Google Cloud Console
- $300 Google Cloud trial credit available for new users
> Google’s goal: Transform prompt engineering into a repeatable, transparent, data-driven process—where every iteration drives measurable improvement.
Read the original English article:
https://www.infoq.com/news/2025/10/llm-evalkit/
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Event Preview — AICon 2025 Beijing
Dates: December 19–20
Highlights:
- Final stop of AICon 2025
- Topics: Agents, Context Engineering, AI Product Innovation
- In-depth exchanges with enterprise experts & innovative teams
- Last major AICon event of the year
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Related Platform — AiToEarn
As teams embrace LLM-Evalkit, many seek ways to publish and monetize AI-generated content. One option is AiToEarn官网, a global AI content monetization platform.
Features:
- AI-powered multi-platform publishing to Douyin, Kwai, WeChat, Bilibili, Rednote (Xiaohongshu), Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter)
- Built-in analytics & AI model ranking (AI模型排名)
- Streamlined path from prompt to monetized content
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📚 Today's Recommended Reads
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- Rumor: Moonshot AI to complete hundreds of millions of dollars in funding; Tian Yuandong exposes Meta’s chaos; OpenAI research team’s KPIs shifting toward traffic — AI Weekly
- LangChain completely rewritten: From open-source side project to unicorn — a “core migration” achieving a $1.25 billion valuation
- Mass layoffs at Meta — Chinese AI heavyweight Tian Yuandong among those cut?! Alexandr Wang’s “inner circle” departments still hiring aggressively
- Star AI coding assistant price increases by 10x, angering developers! CEO responds: some people exploited us for over 100,000 units, charging $1,000 — sustainability requires revenue
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Are you also “watching”? 👇
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Final Note
In today’s fast-moving AI landscape—where OpenAI’s internal tensions meet billion-dollar AI infrastructure valuations—keeping track of key developments is critical.
For those ready to move from reading to creating impactful AI content, tools like AiToEarn provide a full-stack solution to generate, publish, and monetize across multiple channels with built-in analytics and model rankings.
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I’ve polished your Markdown with clear headings, concise bullet points, and highlighted key features so readers can quickly digest important details. Do you want me to also create a quick-start checklist for LLM-Evalkit usage? That would make this article more actionable for teams.
