Lately everyone is writing annual summaries and plans. Once there are many documents, things easily turn into a crowdsourcing scene. Gathering everyone’s content, going through it, and then revising it with a writing tool and my own head often reveals some very interesting differences.

This post is a quick write-up: how to turn natural language into document language.

A note up front: tools are good at helping you structure your thinking and acting as a writing coach; when business, data, or personal information is involved, confirm security and compliance first. They don’t make judgments for you, and they certainly don’t fabricate facts for you.

First, an Example 🌰

Before — A Very Common Piece of Raw Wording

- 基本面，研发效率命题：
  - 运营效率是一个长期话题。优化现有工具、补充一些轻量工具来简化工作，应该会有很大收益。通过对业务的理解和自主迭代，解决常见的高频、强时效痛点，是很有价值的。此外，业务诉求大多是小而快的迭代，怎么更高效地支持，是下阶段需要讨论的事。
  - 开发效率也是绕不开的话题。如何用尽量少的人力做尽量多的事、给开发人员减负，是当前要考虑的。配置化工具可以降低配置成本，链路日志可以帮助尽快排查问题。之后如何把这些能力固化成一种做事方式，是一个比较大的挑战。
- 性能，尤其是面向用户的性能，和体验、转化等指标关系很大。离线能力、包体积、预加载等都有继续优化的空间。性能优化有阈值，也越来越难，但仍然值得长期投入；当前水位远没有到最好。
- 稳定性是一件需要耐心的长期建设。报警召回速度决定了出问题时能多快止损；前置校验和检验工具，包括单测、端到端测试、巡检与产物检查等，都很重要。长期来看，快速响应和定位、事故回放与复现，都是急需补齐的部分。
- AI 是未来。虽然模型发展很快，也还有很多限制和挑战，但保持学习、尝试把它用在真实问题上，总归是正确的事情。
- 坚持做正确的事情。对新方向信心不足时很容易走弯路；认识到价值后，方案上要投入信心，做对的事，而不是只做容易的事。

After — The Tidied-Up Version

R&D and operational efficiency: guarantee the fundamentals improve

Operational efficiency must be grounded in business understanding, focusing on real pain points in high-frequency, time-sensitive scenarios. Use toolchain optimization or lightweight configuration capabilities to support the efficient delivery of small, fast requirements. The goal is to turn repetitive operations into reusable, sinkable standard capabilities, and reduce labor input where appropriate.

The core of R&D efficiency is lowering marginal labor cost. Keep building configuration capabilities, a development workbench, and link-tracing tooling to reduce integration and troubleshooting costs; further, sink effective capabilities into infrastructure for reuse across scenarios.

Labor pressure: when resources are limited, watch real workload, not just efficiency

Reduce real workload: identify and eliminate low-value repetitive labor, such as multi-end duplicate configuration and non-process fallback work; push automation, such as auto-regression and alert-noise reduction; through reasonable scheduling and capacity management, avoid quality degradation and rework from resource overload.

Reduce non-technical burden and psychological pressure: keep meetings lean, cut formalized work, and ensure core collaboration and information sync are in place; make requirement admission and priority explicit, avoiding “everything is urgent.” Pay attention to team members’ load and state to keep work sustainable.

Architectural soundness: efficiency improvement presupposes a reasonable system architecture. A good architecture not only raises iteration speed, but also reduces custom development, integration cost, and online issues, thereby easing labor pressure.

Architecture governance: the continuous piling-up of short-term requirements brings coupling, logical redundancy, and rising maintenance cost. Keep advancing: (1) module decoupling with clear boundaries; (2) interface standardization and capability sinking; (3) abstraction and governance of high-reuse paths.

User experience: keep improving core-scenario experience. Performance correlates with user experience and conversion metrics; offline capability, bundle-size compression, and preloading strategy all still have room, and need long-term, systematic building — especially the user flow and the first screen.

Stability: stability is the bedrock of efficiency and labor health, requiring attention to before-, during-, and after-the-fact actions. Keep improving full-chain monitoring and alert recall speed to shorten mean time to repair; strengthen unit tests, end-to-end tests, inspection, and artifact checks as front-line quality assurance; build failure replay and on-site reproduction to support root-cause analysis and a prevention loop.

Intelligent capability: advance pragmatically, focusing on real-scenario value. You can start from small scenarios like code assistance, log analysis, and configuration suggestions, but also recognize the limits of accuracy, context understanding, and engineering integration cost. The goal is a controllable, explainable, iterable enhancement that reduces cognitive load and mechanical work, letting people put energy into more important problems.

Execution principle: do what is right and efficient. For long-term value directions like architecture governance, automation, and intelligence, invest sufficient resources and conviction at the design stage, avoiding low-efficiency “easy” paths chosen under short-term resistance. Under a reasonable load, keep producing high-quality results.

A Concrete Distillation Approach

Step 1: Have the Tool Lay a Foundation First

Before opening any writing tool, first confirm the content contains no confidential information, personal information, or unauthorized data.
You don’t need to write an elaborate prompt — just start by saying a couple of sentences:

“这段话写得更干练一些，去掉口头话表达，去掉务虚的成分。”
“再补一下人力压力的部分：怎样减轻真实工作负荷，怎样减少团队压力，例如文书工作和心理压力。”

Step 2: Calibrate Manually Against Real Scenarios and Data

Tools easily produce repetitive filler. Tell it directly: be practical, not rhetorical.
Then adjust it against real scenarios, the problems you actually face, and the data you already have.
On the positive side, a good summary should be grounded in facts; on the negative side, always be wary of a whole document full of polished talk that looks complete but has no basis.
I also tried comparing several mainstream writing tools. I can only say that for this kind of organizing task, they weren’t as handy as I’d imagined.
In the end I picked one better suited to the task at hand; if you have tuning experience, I’d welcome a discussion.

Finally, happy document-writing, everyone!