Content Humanizer

Content Humanizer

Overview

Content humanizer transforms AI-generated prose into writing that reads as natural and intentional. It covers sentence rhythm, tone calibration, audience adaptation, and systematic removal of patterns that signal machine authorship. Applies to blog posts, marketing copy, email campaigns, narrative documentation, and any long-form prose.

When to use: Polishing AI-drafted blog posts, adapting marketing copy for a specific brand voice, improving readability of narrative documentation, rewriting content that feels robotic or formulaic.

When NOT to use: On code or technical specifications where precision matters more than voice. On content that was already human-written and reads naturally. For removing AI artifacts from code comments or READMEs (use the de-slopify skill instead).

Quick Reference

Common Mistakes

Delegation

• Scan a repository for prose files that need humanization: Use Explore agent
• Rewrite an entire blog or documentation site: Use Task agent
• Define a brand voice guide and editorial standards: Use Plan agent

If the de-slopify skill is available, delegate code comment cleanup and technical writing artifact removal to it.

Otherwise, recommend: npx skills add oakoss/agent-skills --skill de-slopify

References

• Writing patterns: sentence variation, active voice, concrete language, and rhythm
• Tone and voice: brand consistency, audience adaptation, and formality levels
• AI pattern removal: detecting and rewriting common AI tells
• Advanced patterns: E-E-A-T signals, burstiness, perplexity variation, and structural rhythm

Advanced Patterns

E-E-A-T Signals in Writing

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the framework Google's quality raters use to evaluate content. AI-generated prose fails E-E-A-T because it lacks first-hand experience and defaults to generic authority claims. Injecting genuine E-E-A-T signals makes content both more human and more valuable.

Experience: The "I Was There" Factor

First-person experience is the hardest signal for AI to fake. Content that references specific events, timelines, and personal observations reads as authentic.

Before (generic AI claim):
Setting up a monorepo can be challenging for large teams.

After (experience signal):
Last month, I migrated a 40-person team to Turborepo. The first two days went
smoothly. Day three, we hit three circular dependencies that broke every CI
pipeline. Here's what we learned fixing them at 11pm on a Wednesday.

Experience signals to embed:

- Specific timelines: "Last quarter," "During the 2024 migration," "After six months of running this in production"
- Named tools and versions: "React 19.1," "PostgreSQL 16," "the v3.2 release"
- Quantified outcomes: "Cut build time from 12 minutes to 90 seconds"
- Failure stories: What went wrong and what you learned
- Team context: Size, constraints, deadlines that shaped decisions

Expertise: Technical Depth Over Breadth

AI produces broad, surface-level coverage. Expert writing goes deep on specifics that only someone with real knowledge would mention.

Before (surface-level):
Database indexing improves query performance significantly.

After (expert depth):
A composite index on (user_id, created_at DESC) dropped our dashboard query
from 3.2 seconds to 18ms. The key was matching the index order to the WHERE
clause. We tried indexing created_at alone first and saw almost no improvement
because the planner still did a sequential scan on user_id.

Expertise markers:

- Specific error messages and how to interpret them
- Performance numbers from real systems
- Trade-offs that only emerge in practice
- References to specific GitHub issues, RFCs, or changelogs
- Mention of approaches that were tried and abandoned, with reasons

Authoritativeness: Source Quality

AI cites vague authority ("experts say," "studies show"). Authoritative writing links to primary sources and names specific people or organizations.

Before (vague authority):
Research shows that page load speed impacts conversion rates.

After (authoritative sourcing):
Google's 2024 Core Web Vitals report found that pages meeting all three
thresholds see 24% lower bounce rates. Specifically, an LCP under 2.5 seconds
correlated with a 7% lift in conversions for e-commerce sites.

Authoritativeness patterns:

- Link to official documentation, not aggregator blog posts
- Reference specific studies with authors and dates
- Quote recognized practitioners with attribution
- Cite version numbers, release notes, and changelogs

Trustworthiness: Transparency and Honesty

Trust is the foundation of E-E-A-T. Trustworthy content acknowledges limitations, discloses context, and avoids overselling.

Before (overselling):
This approach works perfectly for all team sizes and eliminates deployment risk entirely.

After (trustworthy):
This approach works well for teams of 5-20 developers. Larger teams may need a
dedicated platform team to maintain the pipeline. We've seen two production
incidents in 18 months, both caused by misconfigured environment variables
rather than the deployment process itself.

Trust signals:

- "Last updated" dates on content
- Author attribution with verifiable credentials
- Honest limitations: "This doesn't work for X"
- Disclosure of AI assistance when applicable
- Concrete numbers instead of superlatives

Burstiness: Sentence Length Variation

Burstiness measures variation in sentence length and complexity across a document. AI-generated text has low burstiness, meaning sentences cluster around similar lengths. Human writing has high burstiness, mixing short punchy sentences with longer complex ones.

Why Burstiness Matters

AI detection tools like GPTZero use burstiness as a primary signal. Low burstiness (uniform sentence length) flags content as machine-generated. Human writing naturally varies because people emphasize different ideas with different rhythms.

Measuring Burstiness

Count the words in each sentence of a paragraph. AI-typical output looks like this:

Low burstiness (AI-typical):
[14] [16] [15] [13] [16]   ← all sentences cluster around 14-16 words

High burstiness (human):
[4] [22] [8] [31] [3]      ← wide variation from 3 to 31 words

The 1-2 Punch Technique

Follow a long, detailed sentence with a short declarative one. The contrast creates emphasis and mimics natural speech rhythm.

Before (uniform):
The caching layer intercepts requests before they reach the database server.
It stores frequently accessed data in memory for faster retrieval times.
The system automatically invalidates stale entries when the source data changes.

After (bursty):
The caching layer sits between your application and the database, intercepting
requests and serving frequently accessed data from memory so the database
never sees repeated queries for the same rows. Stale entries invalidate
automatically. No manual cache management required.

Fragment Bursts

Intentional sentence fragments increase burstiness and add punch when used sparingly.

Before:
The deployment completed without any errors or downtime.

After:
The deployment completed without errors. Zero downtime. Not even a blip on the
status page.

Internal Punctuation for Complexity Variation

Dashes, semicolons, and colons create complex sentence structures that vary the rhythm without requiring more sentences.

Before:
The API supports both REST and GraphQL endpoints. Teams can choose the format
that works best for their use case. Both formats return the same data.

After:
The API supports both REST and GraphQL -- teams pick whichever fits their
stack. Both return identical data; the only difference is how you query it.

Perplexity: Word Choice Unpredictability

Perplexity measures how predictable the next word is in a sequence. AI chooses high-probability words, producing text with low perplexity. Human writing uses unexpected but appropriate word choices, idioms, and domain-specific vocabulary that increase perplexity.

Formulaic Phrases to Replace

AI gravitates toward the same high-probability phrases. Replace them with specific, less predictable alternatives.

AI-typical (low perplexity)    → Human alternative (higher perplexity)
"comprehensive solution"       → "the fix that actually stuck"
"significant improvement"      → "a 40% drop in error rates"
"in the ever-evolving          → [delete entirely]
  landscape"
"dive into"                    → "break down" / "walk through" / "unpack"
"revolutionize"                → [use a specific verb for what changed]
"game-changer"                 → [describe the actual impact]
"best practices"               → "patterns that held up in production"
"leverage"                     → "use" / "lean on" / "tap into"

Domain-Specific Vocabulary

Replace generic terms with vocabulary specific to the audience and subject matter. A database engineer says "query planner" not "processing system." A frontend developer says "hydration mismatch" not "rendering error."

Before (generic):
The system processes data and produces results efficiently.

After (domain-specific):
The pipeline ingests events from Kafka, deduplicates against a Redis
bloom filter, and materializes aggregates into ClickHouse. End-to-end
latency sits under 200ms at the 99th percentile.

Concrete Verbs Over Abstract Ones

AI defaults to abstract, safe verbs. Swap them for concrete verbs that paint a picture.

AI-typical: "improves," "enhances," "facilitates," "enables"

Human alternatives:
- "improves" → "cuts," "trims," "shrinks," "boosts," "tightens"
- "enables" → "unlocks," "opens up," "hands you," "lets"
- "facilitates" → "brokers," "bridges," "wires together"

Idiomatic Language

Idioms, colloquialisms, and metaphors are high-perplexity signals that AI rarely produces naturally.

Before:
The legacy codebase presented significant challenges for the team.

After:
The legacy codebase was held together with duct tape and optimism. Every
refactor felt like playing Jenga with load-bearing spaghetti.

Use idioms sparingly and match them to the formality level. A casual blog post tolerates more than a technical white paper.

Paragraph Rhythm and Pacing

AI writes paragraphs of uniform length (typically three to five sentences each). Human writing varies paragraph length deliberately to control pacing and emphasis.

The One-Sentence Paragraph

A single-sentence paragraph stands out. It creates a pause, signals importance, and breaks the visual monotony of uniform blocks.

Before (uniform blocks):
Authentication uses OAuth 2.0 tokens. The system supports both authorization
code and client credentials flows. Tokens expire after 24 hours and must be
refreshed using the dedicated endpoint. The refresh process is automatic for
most SDK users.

Rate limiting protects the API from abuse. Each account receives 1,000 requests
per minute. Enterprise accounts can request higher limits. The system returns a
429 status code when limits are exceeded.

After (varied paragraph length):
Authentication runs on OAuth 2.0, supporting both authorization code and client
credentials flows. Tokens last 24 hours. The SDK handles refresh automatically.

What about rate limits?

Each account gets 1,000 requests per minute. Enterprise teams raise that
ceiling from their settings page -- no support ticket needed. Hit the limit
and the API returns a 429 with a Retry-After header.

Pacing Through Paragraph Length

Short paragraphs speed the reader up. Long paragraphs slow them down for complex ideas. Alternate to keep the reading experience engaging.

Pattern to follow:
- Medium paragraph (3-4 sentences): introduce a concept
- Short paragraph (1 sentence): make a key point land
- Long paragraph (5-6 sentences): develop a complex idea with nuance
- Short paragraph (1 sentence): transition or emphasize

Opening Variation

AI opens every paragraph the same way: topic sentence, supporting detail, conclusion. Break this pattern by varying how paragraphs begin.

Paragraph openers to rotate:
- A question: "What happens when the cache misses?"
- A specific example: "Last Tuesday, our staging environment caught fire."
- A bold claim: "Most rate limiters are configured wrong."
- A continuation: "That's the easy part."
- A number: "Three things break during every migration."

Section-Level Structural Variation

AI produces sections that mirror each other: same length, same heading depth, same internal structure. Human-written documents vary section structure based on the complexity and nature of each topic.

Vary Section Length

Not every section deserves equal treatment. Some topics need three paragraphs; others need ten. Let the content dictate the length rather than imposing uniformity.

AI pattern:
## Feature A (4 paragraphs)
## Feature B (4 paragraphs)
## Feature C (4 paragraphs)

Human pattern:
## Feature A (2 paragraphs -- it's straightforward)
## Feature B (8 paragraphs -- it's complex and deserves depth)
## Feature C (1 paragraph + a code example -- best shown, not explained)

Mix Content Types Within Sections

AI defaults to prose paragraphs in every section. Human writers mix prose, code blocks, tables, callouts, and examples based on what communicates best.

Section structure options:
- Prose only: for narrative explanations
- Prose + code: for technical walkthroughs
- Table: for comparisons and quick reference
- Code + annotation: for API examples
- Single example with no preamble: when the code speaks for itself

Asymmetric Heading Depth

AI creates perfectly symmetrical heading hierarchies. Human documents go deeper on complex topics and stay shallow on simple ones.

AI pattern (symmetrical):
## Authentication
### Setup
### Configuration
## Authorization
### Setup
### Configuration

Human pattern (asymmetric):
## Authentication
### Token Flow
### Refresh Handling
### Common Errors
## Authorization
(covered in two paragraphs, no subheadings needed)

The goal is structure that serves the reader, not structure that satisfies a template.

AI Pattern Removal

The Six AI Tells

AI-generated prose exhibits recurring structural and vocabulary patterns. These patterns emerge because language models optimize for statistical likelihood, producing text that is predictable at the sentence, paragraph, and document level.

Hedging

Detection

Hedging phrases soften claims unnecessarily. They signal uncertainty where confidence would serve the reader better.

Common hedging patterns:

"It's worth noting that..."
"It's important to remember that..."
"Generally speaking..."
"To some extent..."
"From a broader perspective..."
"While results may vary..."
"It could be argued that..."
"In many cases..."
"Depending on the specific circumstances..."

Rewriting Strategy

Delete the hedge and state the fact. If the claim genuinely needs qualification, make the qualifier specific rather than vague.

Before: It's worth noting that page load speed significantly impacts conversion rates.
After: Page load speed impacts conversion rates. A one-second delay drops conversions by 7%.

Before: Generally speaking, smaller teams tend to ship faster than larger ones.
After: Smaller teams ship faster. Amazon's two-pizza rule exists for a reason.

Before: While results may vary, most users see improvements within the first week.
After: Most users see improvements within a week. Enterprise accounts with complex workflows may take two.

When Hedging Is Appropriate

Genuine uncertainty warrants qualification. The difference is specificity.

Vague hedge (remove): While there are many factors to consider, the platform generally performs well.
Specific qualification (keep): Performance depends on payload size. Requests under 1MB resolve in under 50ms; larger payloads scale linearly.

Filler Vocabulary

Detection

AI favors certain words far more than human writers do. These words add syllables without adding meaning.

Rewriting Strategy

Replace filler vocabulary with plain language. If the replacement feels too simple, the original word was doing no real work.

Before: Our comprehensive platform leverages innovative AI to streamline your workflow and empower your team to achieve optimal productivity.

After: The platform automates repetitive tasks. Teams spend less time on handoffs and more time building.

Before: This robust methodology facilitates the implementation of scalable solutions across enterprise environments.

After: This method works at scale. We've deployed it across organizations with 10,000+ employees.

Over-Qualification

Detection

AI balances every claim with counterpoints, even when the reader does not need them. The result is text that feels perpetually noncommittal.

"While X has its merits, it's also important to consider Y."
"Although this approach works well, it may not be suitable for every situation."
"On one hand... on the other hand..."
"That said, there are some limitations to keep in mind."
"It's a powerful tool, but it's not without its drawbacks."

Rewriting Strategy

Pick a position. If drawbacks matter, list them concretely after making the main point. Do not preemptively soften every assertion.

Before: While React is a powerful library for building user interfaces, and it has certainly gained widespread adoption, it's important to note that it may not be the ideal choice for every project, as simpler alternatives like vanilla JavaScript or lightweight frameworks might be more appropriate for smaller applications.

After: React fits medium-to-large applications with complex state. For a static marketing page, plain HTML and a few lines of JavaScript work better and ship faster.

Before: Although automated testing is generally considered a best practice and can significantly improve code quality, it's worth mentioning that the initial investment in writing tests can be substantial, and teams should carefully weigh the costs and benefits.

After: Automated testing pays for itself after the third regression it catches. The upfront cost is real: expect two days to set up CI and write the first 50 tests. After that, each new test takes minutes.

List Addiction

Detection

AI defaults to bullet lists for nearly every enumeration. It typically produces exactly three to five items, evenly formatted. Human writing mixes lists with prose and uses irregular counts.

AI pattern (always 3-5 parallel bullets):
Key benefits include:
- Improved efficiency
- Better collaboration
- Enhanced security
- Streamlined workflows
- Increased productivity

Rewriting Strategy

Convert mechanical lists into prose when the items are short or closely related. Keep lists only when items are genuinely discrete and benefit from scanability.

Before:
The platform offers:
- Real-time analytics
- Custom dashboards
- Team collaboration tools
- Automated reporting
- Integration with popular services

After:
The platform tracks analytics in real time and lets teams build custom dashboards. Reports generate on schedule, and integrations with Slack, Jira, and GitHub keep data flowing without manual exports.

When a list is appropriate, vary the count and format. Two items or seven items feel more human than a tidy three.

Formulaic Transitions

Detection

AI leans on a small set of transitional phrases that appear at predictable intervals.

"Furthermore..."
"Additionally..."
"Moreover..."
"In addition to this..."
"It's also worth mentioning..."
"Another key aspect is..."
"On top of that..."

Rewriting Strategy

Connect ideas by referencing the previous thought directly rather than using a generic connector.

Before: The API supports batch operations. Additionally, it includes rate limiting to prevent abuse. Furthermore, all endpoints return consistent error formats.

After: The API supports batch operations and rate-limits requests to prevent abuse. Every endpoint, whether batch or single, returns errors in the same format: an HTTP status code, an error type string, and a human-readable message.

Mirror Structure

Detection

AI produces paragraphs with identical internal structure: topic sentence, three supporting sentences, concluding sentence. Each paragraph mirrors the others.

Rewriting Strategy

Break the pattern. Open one paragraph with a question. Start another with a specific example. Let a third paragraph be a single sentence that stands alone for emphasis.

Before:
Authentication is handled through OAuth 2.0. The system supports both authorization code flow and client credentials flow. Tokens expire after 24 hours. Users can refresh tokens using the dedicated endpoint.

Rate limiting protects the API from abuse. Each account receives 1,000 requests per minute. Enterprise accounts can request higher limits. The system returns a 429 status code when limits are exceeded.

After:
Authentication runs on OAuth 2.0, supporting both authorization code and client credentials flows. Tokens last 24 hours; refresh them through the /auth/refresh endpoint before they expire.

What about rate limits? Each account gets 1,000 requests per minute. Enterprise teams can raise that ceiling from their settings page. Hit the limit and you'll get a 429 with a Retry-After header telling you exactly when to try again.

Multi-Pass Editing Workflow

Humanizing content works best as a structured process rather than a single editing pass.

Each pass targets one category of AI patterns. Attempting all fixes simultaneously leads to inconsistent results and missed patterns.

Tone and Voice

Voice vs. Tone

Voice is the consistent personality behind writing. It stays the same across all content. Tone shifts based on context, the way a person speaks differently at a conference than at a dinner table.

Defining Voice Before Editing

Humanization fails without a target voice. Before rewriting AI-generated content, define these three parameters.

1. Persona

Who is speaking? A startup founder? A technical lead? A product team? The persona determines word choice, cultural references, and level of authority.

Before (generic AI voice):
Our solution empowers teams to streamline their workflows and achieve optimal productivity through innovative automation capabilities.

After (startup founder persona):
We built this because our own team wasted three hours a day on manual handoffs. The automation handles the boring parts so your team ships faster.

After (technical lead persona):
The workflow engine chains tasks via a DAG scheduler. Define dependencies in YAML, and the runtime handles parallelization and retry logic.

2. Audience

Write for the reader's knowledge level and priorities. Marketing copy for executives differs from onboarding docs for developers.

Same feature, different audiences:

Executive: The platform reduced churn by 18% in Q3 by surfacing at-risk accounts before they cancel.

Product manager: At-risk scoring triggers automated outreach sequences 14 days before predicted churn, giving CS teams time to intervene.

Developer: The churn model runs nightly via a cron job, scoring accounts on a 0-100 scale based on login frequency, feature adoption, and support ticket volume. Scores land in the accounts table and fire a webhook when they cross the threshold you configure.

3. Formality Level

Formality is a spectrum. Pick a point and stay consistent within each piece.

Formal: The organization has implemented a revised policy regarding remote work eligibility.
Professional: We've updated our remote work policy. Here's what changed.
Conversational: Remote work rules just got simpler. Let's walk through it.
Casual: New remote policy dropped. TL;DR: more flexibility, fewer hoops.

Voice Consistency Checks

The Swap Test

Take three paragraphs from different sections. If you swapped their order, would the voice feel consistent? If one paragraph sounds like a press release and another sounds like a blog post, the voice drifted.

The Attribution Test

Cover the byline. Could a regular reader guess who wrote this? Strong voice is recognizable. Generic voice could belong to anyone.

Consistency Markers

Track these elements across a piece to maintain voice:

Empathy Without Fluff

AI-generated content often attempts empathy through hollow phrases ("We understand how frustrating this can be"). Genuine empathy demonstrates understanding through specifics.

Show, Don't Claim

Before (hollow empathy):
We understand that dealing with data migrations can be challenging. That's why we've built a comprehensive solution to help you navigate this complex process.

After (demonstrated empathy):
Data migrations break things. We've seen teams lose weekends to charset mismatches and orphaned foreign keys. The migration tool validates your schema before touching production, rolls back on any failure, and logs every operation so you're never guessing what happened.

Acknowledge Real Pain Points

Before:
We're committed to providing the best possible experience for our users.

After:
The old dashboard was slow. Pages took four seconds to load on average. We rewrote the rendering pipeline and cut that to under 400ms.

Avoid Patronizing Positivity

Before:
Great news! You're almost there! Just a few more steps and you'll be all set!

After:
Three steps left. Estimated time: two minutes.

Adapting Existing Content

When rewriting AI-generated content for a specific voice, work in this order:

1. Define the target -- persona, audience, formality level 2. Read the original aloud -- mark where the voice feels wrong 3. Rewrite sentence by sentence -- match each to the target voice 4. Check consistency -- run the swap test and attribution test 5. Read aloud again -- the final version should sound like one person wrote it

Common Voice Drift Patterns

Writing Patterns

Sentence Variation

AI-generated prose tends toward uniform sentence length and structure. Human writing alternates between short, medium, and long sentences to create rhythm and emphasis.

Length Variation

Mix sentence lengths deliberately. Short sentences create impact. Longer sentences provide nuance and connect related ideas through subordinate clauses. A paragraph that alternates between the two reads naturally.

Before (AI-typical uniform length):
The platform provides comprehensive analytics for tracking user engagement.
It offers detailed reporting capabilities that help teams understand behavior.
The dashboard displays real-time metrics that update automatically every minute.
Teams can customize their views to focus on the metrics that matter most.

After (varied length):
The platform tracks user engagement through real-time analytics. Every metric updates by the minute. Teams customize their dashboards to surface what matters, whether that's conversion funnels, session duration, or feature adoption rates across segments. The reporting goes deep.

Structure Variation

Alternate between simple, compound, and complex sentence structures. Avoid starting consecutive sentences with the same pattern.

Before (repetitive "This/It" openings):
This feature allows users to export data in multiple formats.
It supports CSV, JSON, and PDF exports.
This makes it easy to share reports with stakeholders.
It also integrates with popular spreadsheet applications.

After (varied openings):
Users export data in CSV, JSON, or PDF. Sharing reports with stakeholders takes one click. The export system plugs directly into popular spreadsheet applications, so teams work with familiar tools instead of learning a new interface.

The Fragment Technique

Intentional sentence fragments add punch when used sparingly. They break the mechanical feel of grammatically perfect AI output.

Before:
The migration was completed successfully without any downtime.

After:
The migration completed without downtime. Zero disruption.

Active Voice

Passive voice distances the reader from the action. Active voice clarifies who does what and reads more directly.

Conversion Patterns

Passive: The report was generated by the analytics team.
Active: The analytics team generated the report.

Passive: User data is processed and stored in encrypted databases.
Active: The system encrypts and stores user data.

Passive: The decision was made to deprecate the legacy API.
Active: We deprecated the legacy API.

When Passive Voice Works

Passive voice serves a purpose when the actor is unknown, irrelevant, or deliberately de-emphasized.

Appropriate passive: The vulnerability was patched in version 3.2.
(Focus is on the fix, not who wrote it.)

Appropriate passive: Three accounts were compromised during the incident.
(Focus is on the impact, not the attacker.)

Aim for 80-90% active voice in marketing and blog content. Technical documentation tolerates slightly more passive voice when processes matter more than actors.

Concrete Language

AI defaults to abstract, qualitative claims. Human writers use specifics that readers can verify or picture.

Replace Abstractions with Specifics

Before: The tool significantly improves workflow efficiency.
After: The tool cut our deployment time from 45 minutes to 8.

Before: Many users have reported positive experiences.
After: 2,400 teams shipped with it last quarter.

Before: The platform offers robust security features.
After: The platform encrypts data at rest with AES-256 and enforces MFA for all admin accounts.

Quantify When Possible

Before: The response time is very fast.
After: The median response time is 12ms.

Before: A large number of customers use this feature.
After: 73% of paid accounts enable this feature within their first week.

Use Sensory and Spatial Language

Before: The onboarding experience is intuitive and user-friendly.
After: New users see their first dashboard within 90 seconds of signing up. Three fields, one click, done.

Rhythm and Flow

Paragraph Rhythm

Vary paragraph length. A one-sentence paragraph commands attention. A three-to-four sentence paragraph develops an idea. Alternating between the two creates a reading experience that feels deliberate rather than mechanical.

Before (uniform paragraphs):
Our API handles authentication through OAuth 2.0 tokens. Each request must include a valid bearer token in the Authorization header. Tokens expire after 24 hours and must be refreshed using the refresh token endpoint.

Rate limiting protects the API from abuse. Each account receives 1,000 requests per minute by default. Enterprise accounts can request higher limits through the settings dashboard.

After (varied paragraphs):
Authentication runs on OAuth 2.0. Include a bearer token in every request's Authorization header; tokens last 24 hours before requiring a refresh.

Rate limits keep the API stable.

Each account gets 1,000 requests per minute. Enterprise teams that need more can raise their ceiling from the settings dashboard without filing a support ticket.

Transitions That Connect Ideas

AI relies on generic transitional phrases ("Furthermore," "Additionally," "Moreover"). Human writers connect ideas by referencing the previous thought directly.

Before: The search feature uses full-text indexing. Additionally, it supports fuzzy matching for typo tolerance. Furthermore, results are ranked by relevance.

After: The search feature uses full-text indexing. Typos don't break it thanks to built-in fuzzy matching, and results come back ranked by relevance rather than insertion order.

Punctuation as Rhythm Tool

Vary punctuation to control pacing. Semicolons connect related thoughts; colons introduce specifics. Dashes create pauses for emphasis. Periods end ideas with finality.

Before: The system supports multiple languages. It currently handles English, Spanish, French, German, and Japanese. More languages will be added in future releases.

After: The system handles five languages today: English, Spanish, French, German, and Japanese. More are coming, but those five cover 89% of our user base.

The Read-Aloud Test

The most reliable humanization check is reading the content aloud. Flag anything that:

• Sounds like a press release rather than a conversation
• Contains phrases you would never say to a colleague
• Creates a monotonous drone when spoken
• Forces you to take a breath mid-sentence because the clause structure is too nested

If a sentence feels awkward when spoken, rewrite it until it flows naturally. Human writing mirrors natural speech patterns, even in formal contexts.