Plain Writing

Plain writing

This skill captures how the user wants written prose to read. The goal is text that anyone can read once and understand. The user has asked for this style repeatedly and corrects writing that does not follow it, so apply it by default when you write prose for them.

The rules

1. Use simple, everyday words. Prefer the common word over the fancy one, e.g., write "use" rather than "leverage". Short familiar words are faster to read.

2. Write complete sentences. Each sentence states one clear thing and has a subject and a verb. Do not write fragments, and do not stitch several ideas together with colons or semicolons into one dense line. If a sentence is doing two jobs, split it into two.

3. No dashes, and limit colons. Do not use em dashes or en dashes, including in number ranges. Join clauses with a period, or with a word such as "and". Write ranges with the word "to", e.g., "0.94 to 0.96". Use a colon only to introduce a list. Do not use a colon to join clauses or to set up a point, e.g., "Read for the schema: the feature fires". Dashes, and colons used for a point, invite the clever phrasing the user does not want.

4. No jargon. Do not use shorthand from a field when a plain phrase works. If a technical term is truly needed, say it once and explain it in plain words. Avoid a word such as "calibrated" unless you define it simply.

5. No analogies or imagery. Do not explain something by comparing it to a different thing. Do not use a metaphor or any phrase meant to sound smart. Describe the actual thing in literal terms.

6. No filler. Cut words and phrases that add nothing, e.g., "it is worth noting that". Every sentence should add something the reader needs.

7. Explain things fully and clearly. Plain also does not mean terse. If an idea is compressed into one cramped sentence, expand it so each point gets its own sentence and the reader can follow it. When you have several distinct things to list, give each one a clear sentence or its own bullet, not one long line. Clarity comes before both shortness and length.

8. Do not give inanimate things human actions. A paper cannot do something that only a person can do. Name the person who does the action, e.g., write "the authors argue" rather than "the paper argues". A plain factual verb for an inanimate subject is fine, e.g., "the table shows the scores".

9. Do not invent hyphenated adjectives. A common compound adjective that people already use is fine, e.g., "well-crafted". Avoid a phrase you make up by joining words with a hyphen to sound compact or clever, e.g., "reveal-style colon". When you catch yourself coining one, reword it in plain words. A good test is whether you would find the term in a dictionary or hear it in normal speech. If not, write it out.

10. Do not pad with empty emphasis words. Words like "really" and "real" add emphasis but no information, so drop them. Do not say that something "matters" or "carries weight". State the actual point, or cut the sentence.

11. Keep lists and examples simple.

    • Do not write a three-part series in a sentence, e.g., "it is simple, clear, and direct". It sounds practiced. When you have items to list, use a bullet list. Do not pad a list to three just for rhythm.
    • When you use an example to make a point, give one example and introduce it with "e.g.". Do not stack several examples for the same point.

How to revise

Revise in two passes.

First pass. Read the text once and fix anything that breaks the rules above.

Second pass. Read the result again as if you had never seen it. Go clause by clause and ask whether each clause adds something the reader needs. If a clause or a whole sentence does not earn its place, remove it. Then check that a reader seeing the text for the first time would understand every sentence.

The revision artifact

When the second pass removes or rewrites anything, also make a small HTML file so the user can see what changed. Skip this for tiny edits where the second pass did not cut or rewrite anything.

Build a list of the changes at the level of whole sentences. Group the entries into paragraphs, and give each paragraph a "para" number. Each entry is one of these kinds:

• keep. The sentence is unchanged. Fields: type is "keep", and text.
• edit. The sentence was rewritten. Fields: type is "edit", old, new, and why.
• del. The sentence was removed. Fields: type is "del", plus old and why.

The why is a short plain reason for the change, e.g., "filler, adds nothing". Here is the shape of the list:

[
  { "para": 1, "items": [
    { "type": "edit", "old": "...", "new": "...", "why": "..." },
    { "type": "del",  "old": "...", "why": "..." }
  ]},
  { "para": 2, "items": [
    { "type": "keep", "text": "..." }
  ]}
]

Then take the template at assets/revision_template.html, replace the text __DATA__ with this list as JSON, and save the result to a new file in /tmp, e.g., /tmp/revision-<short-name>.html. Do not write it into the skill folder. Tell the user where the file is. The file has three tabs:

• First draft
• Second draft
• Diff

In the Diff tab the removed text is red and the rewritten text is green. The reason for each change appears when the user hovers the colored text.

Examples

These are before and after pairs from the user's own edits.

Example 1. Dashes and jargon. Before: Fresh-annotation re-scoring (cache bypassed) moved means by less than 0.004. Read for the schema: "feature fires" is a calibrated proxy for "the property holds" at F1 of 0.94 to 0.96 for coherent features. After: We ran the scoring again with new language model calls and no caching. The average scores changed by less than 0.004, so they are not an effect of caching. For most features, the description agrees with how the feature actually behaves, at an F1 of about 0.94 to 0.96.

Example 2. Filler. Before: It is worth noting that the second pass actually removes quite a lot of words, and this matters. After: The second pass removes a lot of words.

Example 3. One cramped sentence split into clear ones. Before: The groups the features were sorted into were the authors' own reading, the example posts were written by hand, and finer detail meant training extra small models and labeling again. After: First, the authors sorted the features into groups themselves, based on their own reading of the results. Second, they wrote the example posts by hand after reading many of the posts. Third, when they wanted finer detail, they trained another small model and labeled the posts again.

Example 4. Analogy removed. Before: The feature index is like a card catalog that the optimizer can flip through. After: The feature index is a list of named features. The optimizer can look up which feature matches a request.

Example 5. Inanimate subject given a human action. Before: The paper argues that the trend holds, and the result bears emphasis. After: The authors argue that the trend holds.

Example 6. A group of three. Before: Review time on a big change dropped from a full day to under an hour, weekly output went up threefold, and the rate of bugs per change fell. After: Several measures of productivity improved, e.g., review time on a big change dropped from a full day to under an hour.

Example 7. Empty importance words. Before: This result matters, and it carries weight for the design. After: As a result, the system can skip the model on most documents.