Editing & Style with AI

🔄 Quick Recall: In the previous lesson, you generated code documentation — READMEs, comments, changelogs, and ADRs. Now you’ll learn to edit and polish all types of technical content using plain language principles, style guides, and AI-assisted editing.

Writing is rewriting. The first draft gets the information down; editing makes it clear, consistent, and readable. AI transforms editing from a manual, page-by-page process into a systematic one — applying style rules across entire documentation sets in seconds.

Plain Language Principles

AI prompt for plain language editing:

Edit this technical content for plain language. Original: [PASTE CONTENT]. Apply these rules: (1) Replace complex words with simple ones (utilize → use, facilitate → help, implement → set up), (2) Shorten sentences — target 15-20 words per sentence, (3) Use active voice — the subject does the action (not “the file is opened by the system” but “the system opens the file”), (4) Remove filler words (very, really, basically, essentially, in order to, as a matter of fact), (5) Front-load important information — put the key point first in each paragraph, (6) One idea per sentence. Show the before/after for each change with a brief explanation of why.

Common plain language substitutions:

Style Guide Enforcement

AI prompt for style guide compliance:

Check this documentation against our style guide. Documentation: [PASTE CONTENT]. Style guide rules: [PASTE RULES OR DESCRIBE — e.g., “Use ‘click’ not ‘select,’ write numbers under 10 as words, use Oxford comma, capitalize product names, write in second person (you)”]. For each violation: (1) the specific text, (2) the rule it violates, (3) the corrected text. Then provide a compliance score: percentage of text that follows the style guide.

✅ Quick Check: Your style guide says “use second person (you)” but a document says “One should configure the settings before proceeding.” What’s the rewrite? (Answer: “Configure the settings before proceeding” or “You should configure the settings before proceeding.” Second person is more direct and engaging. “One should” is formal academic style that creates distance between the documentation and the reader.)

Readability Analysis

AI prompt for readability assessment:

Analyze the readability of this technical content. Content: [PASTE]. Assess: (1) Flesch-Kincaid reading level — target grade 8-10 for general tech docs, grade 10-12 for developer docs, (2) Average sentence length — target 15-20 words, (3) Paragraph length — target 3-5 sentences, (4) Passive voice percentage — target under 20%, (5) Jargon density — how many terms require domain knowledge. Provide: an overall readability score, the 5 least readable sentences with rewrites, and specific suggestions to improve each metric.

Consistency Audit

AI prompt for doc-wide consistency:

Audit this documentation set for consistency issues. Documents: [PASTE OR DESCRIBE]. Check for: (1) Terminology — same concept called different names in different places, (2) Formatting — inconsistent use of bold, code blocks, headings, lists, (3) Voice and tone — some sections formal, others casual, (4) Capitalization — product names, features, and headings inconsistently capitalized, (5) Date and number formats — inconsistent formatting across pages. Generate: a list of every inconsistency with the location, the conflicting usages, and the recommended standard.

Editing Workflow

AI prompt for editorial review:

Review this documentation page as a technical editor. Content: [PASTE]. Check for: (1) Accuracy — are the technical claims correct based on what you can verify? (2) Completeness — are there obvious gaps or missing steps? (3) Clarity — are there sentences that could be misunderstood? (4) Structure — is the information in a logical order? (5) Audience fit — is the language appropriate for the target audience? For each issue: the specific text, why it’s a problem, and the suggested revision. Rate the overall quality: publish-ready, needs minor edits, or needs major revision.

Key Takeaways

• Plain language reduces reading time by 30% and comprehension errors by 50% — words like “utilize,” “facilitate,” and “in order to” add characters without adding meaning. AI applies plain language rules systematically across entire documents
• Terminology inconsistency (calling the same thing different names) is the most common doc quality issue and generates avoidable support tickets — AI enforces a terminology guide across thousands of pages in seconds
• Active voice is clearer than passive (“the system processes data” vs. “data is processed”) — but passive voice is appropriate when the actor is unknown or when the result matters more than the actor. AI applies this nuanced rule at 80%+ active voice
• Style guide enforcement at scale requires automation — no human editor can consistently check capitalization, terminology, voice, and formatting across 50+ pages. AI achieves 95%+ compliance by checking every sentence against the ruleset
• Editing is where documentation quality lives: the first draft gets information on the page, editing makes it clear, consistent, and trustworthy. Budget as much time for editing as for writing

Up Next

In the next lesson, you’ll build documentation systems — templates, review workflows, versioning, and automated freshness checks that keep documentation current at scale.