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

- The fundamentals, the R&D efficiency proposition:
  - Operational efficiency is a long-term topic. Optimizing existing tools and adding some lightweight tools to simplify work should bring big gains. Solving common high-frequency, time-sensitive pain points through business understanding and self-iteration is very valuable. Besides, most business requests are small, fast iterations, so how to support them more efficiently is something to discuss in the next stage.
  - Development efficiency is another topic we can't avoid. How to do as much as possible with as little manpower as possible, and lighten the load on developers, is what we need to think about now. Configuration-based tooling can lower configuration cost, and link tracing helps locate problems quickly. How to later solidify these capabilities into a way of working is a fairly big challenge.
- Performance, especially user-facing performance, is closely tied to metrics like experience and conversion. Offline capability, bundle size, preloading — all still have room to optimize. Performance optimization has a ceiling and keeps getting harder, but it is still worth long-term investment; the current level is far from the best.
- Stability is a long-term effort that needs patience. Alert recall speed determines how quickly we can stop the bleeding when something goes wrong; front-line checks and validation tools — including unit tests, end-to-end tests, inspection, and artifact checks — are all important. In the long run, fast response and localization, and incident replay and reproduction, are all parts we urgently need to fill in.
- AI is the future. Although models are developing fast, there are still many limits and challenges, but keeping learning and trying to apply it to real problems is the right thing to do.
- Keep doing the right thing. When confidence in a new direction is low, it is easy to go astray; once you recognize the value, invest conviction in the approach and do the right thing, not just the easy thing.

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

  1. Before opening any writing tool, first confirm the content contains no confidential information, personal information, or unauthorized data.
  2. You don't need to write an elaborate prompt — just start by saying a couple of sentences:
"Make this passage more concise — cut the spoken-style phrasing and the abstract filler."
"Also add the part on manpower pressure: how to lighten the real workload and reduce team stress, such as paperwork and psychological pressure."

Step 2: Calibrate Manually Against Real Scenarios and Data

  1. Tools easily produce repetitive filler. Tell it directly: be practical, not rhetorical.
  2. Then adjust it against real scenarios, the problems you actually face, and the data you already have.
  3. 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.
  4. 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.
  5. 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!