Adjusting Vocabulary Level
Control complexity: simple 8th-grade language vs domain-specific terminology.
Adjusting Vocabulary Level is a free AI Prompt Engineering lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Prompt Engineering learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Vocabulary: The Readability Dial
Vocabulary level is the single most powerful factor in how accessible or exclusive your content feels. A simple word choice change can make the difference between a response that connects with the reader and one that alienates them.
AI models can operate across the full vocabulary spectrum — from kindergarten-simple to doctoral-complex — but you must set the dial explicitly.
Simple Language, No Jargon
Requesting simple, jargon-free language is critical for consumer-facing content, support documentation, and cross-functional communication.
Effective requests:
- 'Use simple language, no technical jargon'
- 'Explain as if the reader has no background in this field'
- 'Every technical term must be explained in plain English when first used'
- 'Replace any word a typical 10-year-old would not know'
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=150,
messages=[{
'role': 'user',
'content': (
'Explain what encryption is. '
'Simple language only — no technical jargon. '
'If you must use a technical word, immediately define it in plain English in parentheses. '
'Target reading level: general public with no tech background. '
'3 sentences maximum.'
)
}]
)
print(response.content[0].text)Reading Level Specifications
The Flesch-Kincaid Grade Level measures reading difficulty by US school grade. You can request it directly:
- '8th-grade reading level' → accessible to most adults
- '5th-grade reading level' → simple enough for children or non-native speakers
- '12th-grade reading level' → educated adult, some complexity acceptable
- 'College reading level' → assumes post-secondary education
Grade-level requests work remarkably well for controlling vocabulary complexity.
import openai
client = openai.OpenAI(api_key='sk-your-key-here')
levels = ['5th-grade', '8th-grade', '12th-grade', 'college']
for level in levels:
response = client.chat.completions.create(
model='gpt-4o',
max_tokens=60,
messages=[{
'role': 'user',
'content': (
f'Explain what a database index is. '
f'Reading level: {level}. 2 sentences maximum.'
)
}]
)
print(f'[{level}]: {response.choices[0].message.content.strip()}')
print()Assuming Expert Knowledge
When writing for domain experts, jargon is not a problem — it is efficiency. Experts prefer precise technical terminology over simplified explanations.
Requests for expert-level vocabulary:
- 'Assume expert knowledge — no definitions required'
- 'Use technical terminology freely'
- 'Write for an audience of PhD researchers in this field'
- 'No hand-holding — assume the reader knows the fundamentals'
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
expert_prompt = (
'Explain the trade-off between MVCC and locking-based concurrency control in OLTP databases.\n'
'Audience: senior database engineers with 10+ years experience.\n'
'Assume expert knowledge — no definitions of basic terms needed.\n'
'Use technical terminology freely: transaction isolation levels, write skew, '
'phantom reads, lock contention, WAL, tuple visibility.\n'
'Max 4 sentences. Be precise, not introductory.'
)
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=200,
messages=[{'role': 'user', 'content': expert_prompt}]
)
print(response.content[0].text)Including Technical Terminology
When writing educational content for learners who need to acquire technical vocabulary, you can instruct the model to:
- Introduce technical terms progressively
- Bold each term on first use
- Always pair a technical term with its plain-English equivalent
- Build a mini-glossary at the end
This approach teaches vocabulary while communicating the content.
import openai
client = openai.OpenAI(api_key='sk-your-key-here')
response = client.chat.completions.create(
model='gpt-4o',
messages=[{
'role': 'user',
'content': (
'Explain how HTTPS works for a developer just starting to learn web security. '
'Vocabulary strategy:\n'
'- Introduce technical terms gradually — start simple, add complexity\n'
'- Bold every new technical term on first use\n'
'- Immediately follow each bold term with a plain-English definition in parentheses\n'
'- End with a 4-term Glossary section\n'
'Length: 150 words max (excluding glossary).'
)
}]
)
print(response.choices[0].message.content)The Jargon Audit
A jargon audit asks the model to review existing content and flag or replace technical terms that a non-expert might not understand.
Useful for: reviewing your own writing before sending to a non-technical audience, checking support documentation, auditing marketing copy for accessibility.
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
technical_text = (
'Our platform leverages a microservices architecture with asynchronous event-driven '
'communication via a Kafka message broker. Each service exposes RESTful endpoints '
'behind an API gateway that handles rate limiting and JWT authentication. '
'Horizontal pod autoscaling on Kubernetes ensures elasticity under load.'
)
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=300,
messages=[{
'role': 'user',
'content': (
'Perform a jargon audit on the following paragraph.\n'
'1. List every technical term a non-technical business stakeholder might not know.\n'
'2. For each term, provide a plain-English alternative (5 words or fewer).\n'
'3. Rewrite the paragraph using only the plain-English alternatives.\n\n'
f'Text:\n{technical_text}'
)
}]
)
print(response.content[0].text)Vocabulary Consistency
Consistent vocabulary across a document is especially important in technical writing. When the same concept has multiple names in common use, pick one and stick with it throughout:
- Do not alternate between 'user', 'customer', and 'member'
- Do not call the same system 'API', 'endpoint', and 'service' interchangeably
- Do not switch between 'database' and 'data store'
Request: 'Use consistent terminology throughout. If in doubt, prefer [X] over [Y] for [concept].'
import openai
client = openai.OpenAI(api_key='sk-your-key-here')
response = client.chat.completions.create(
model='gpt-4o',
messages=[{
'role': 'user',
'content': (
'Write a 3-section product description for our B2B SaaS platform.\n'
'VOCABULARY RULES — use these terms consistently throughout:\n'
'- Use "workspace" not "project", "environment", or "account"\n'
'- Use "team member" not "user" or "collaborator"\n'
'- Use "dashboard" not "panel", "console", or "interface"\n'
'- Use "connect" not "integrate", "link", or "sync"\n'
'Sections: Overview, Key Benefits, How It Works.\n'
'Max 100 words total.'
)
}]
)
print(response.choices[0].message.content)Vocabulary for International Audiences
When writing for global audiences where English may be a second language, vocabulary choices matter even more:
- Prefer shorter, common words over longer, rare ones
- Avoid idioms and phrasal verbs that do not translate
- Avoid cultural references unfamiliar outside one region
- Use explicit phrasing over implied meaning
- Prefer active constructions over passive
Request: 'Write for an international audience where English is a second language — simple vocabulary, no idioms.'
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
current_text = (
'Our platform hits the ground running right out of the box, '
'giving your team a leg up on the competition. '
'Once you are up to speed, you will be firing on all cylinders. '
'The ball is in your court — reach out to kick off your trial.'
)
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=200,
messages=[{
'role': 'user',
'content': (
'Rewrite the following text for an international audience where English is a second language.\n'
'Rules:\n'
'- Replace all idioms with literal, plain alternatives\n'
'- Use only common, short words (prefer "start" over "commence")\n'
'- No phrasal verbs ("kick off" → "start", "reach out" → "contact")\n'
'- Active voice only\n\n'
f'Text:\n{current_text}'
)
}]
)
print(response.content[0].text)Vocabulary Testing with Self-Audit
Ask the model to audit its own output for vocabulary level after generating it. This two-step approach catches vocabulary that slipped through despite your instructions:
- Generate the content with vocabulary instructions
- Ask the model to list any words that violate the vocabulary level and suggest replacements
import openai
client = openai.OpenAI(api_key='sk-your-key-here')
# Step 1: generate with vocabulary constraint
result = client.chat.completions.create(
model='gpt-4o',
messages=[{
'role': 'user',
'content': 'Explain HTTP status codes in 3 sentences for a non-technical business person. Simple words only.'
}]
)
content = result.choices[0].message.content
print('GENERATED:')
print(content)
print()
# Step 2: vocabulary self-audit
audit = client.chat.completions.create(
model='gpt-4o',
messages=[{
'role': 'user',
'content': (
f'Review the following text. List any words a typical non-technical business person '
f'(no tech background) might not know. For each flagged word, suggest a simpler alternative.\n\n'
f'Text:\n{content}'
)
}]
)
print('VOCABULARY AUDIT:')
print(audit.choices[0].message.content)Vocabulary and Trust
Vocabulary level directly affects how much trust readers place in your content:
- Too simple for the audience → feels patronizing, undermines credibility
- Too complex for the audience → feels exclusionary, creates confusion
- Exactly right → feels written specifically for this reader
The goal is not the simplest vocabulary possible — it is the right vocabulary for the specific reader. A blockchain developer reading about Ethereum expects precise terminology; a first-time crypto buyer does not.
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
# Same topic, calibrated vocabulary for different trust contexts
audiences = [
('CRYPTO BEGINNER', 'No technical background, first time reading about cryptocurrency. Plain English, no assumed knowledge.'),
('BLOCKCHAIN DEVELOPER', 'Expert: knows EVM, gas fees, consensus mechanisms, smart contract bytecode. No definitions needed.')
]
topic = 'Why Ethereum uses proof-of-stake instead of proof-of-work.'
for label, spec in audiences:
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=100,
messages=[{
'role': 'user',
'content': f'Audience: {spec}\n\nExplain in 2 sentences: {topic}'
}]
)
print(f'[{label}]')
print(response.content[0].text.strip())
print()Vocabulary and Brand Voice
Brand voice is partly a vocabulary choice. Companies define their vocabulary rules in style guides — and AI can follow those rules consistently if you specify them.
Examples of brand vocabulary rules:
- Mailchimp: 'Say help, not assist'
- Stripe: 'Say set up (verb) but setup (noun)'
- Shopify: 'Say merchants, not customers or users'
Add your brand vocabulary rules to the system message for consistent output across all content.
import anthropic
client = anthropic.Anthropic(api_key='sk-ant-your-key-here')
brand_vocab_system = (
'You write content for NorthBridge, a B2B SaaS company. '
'Brand vocabulary rules:\n'
'- Say "workspace" not "account" or "project"\n'
'- Say "connect" not "integrate" or "link"\n'
'- Say "team" not "users" or "members"\n'
'- Say "get started" not "onboard" or "begin"\n'
'- Avoid: "leverage", "synergy", "ecosystem", "seamlessly"'
)
response = client.messages.create(
model='claude-opus-4-5',
max_tokens=100,
system=brand_vocab_system,
messages=[{
'role': 'user',
'content': 'Write a 2-sentence CTA for the homepage inviting users to create an account and start using integrations.'
}]
)
print(response.content[0].text)Knowledge Check
A company wants to send a data breach notification email to all 50,000 users — most of whom are non-technical consumers. The current draft says: 'A vulnerability in our OAuth2 implementation resulted in unauthorized exfiltration of hashed user credentials.' What is the primary vocabulary problem?
Adjusting Vocabulary Level — Recap
Vocabulary level controls how accessible or authoritative your content feels. Key techniques:
- Request specific reading levels: 5th-grade, 8th-grade, college
- Use 'no jargon' for consumer-facing and cross-functional content
- Use 'assume expert knowledge' to enable precise technical terminology
- Teach vocabulary with progressive introduction and bold-on-first-use
- Request vocabulary consistency across all documents
- Write for international audiences by removing idioms and phrasal verbs
- Use self-audit prompts to catch vocabulary violations in generated content
Frequently asked questions
Is the “Adjusting Vocabulary Level” lesson free?
Yes — the full text of “Adjusting Vocabulary Level” is free to read here on the web, and the AI Prompt Engineering course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Prompt Engineering course, upgrade to CoddyKit PRO.
What will I learn in “Adjusting Vocabulary Level”?
Control complexity: simple 8th-grade language vs domain-specific terminology. You practise AI Prompt Engineering with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Prompt Engineering?
No prior experience is required. AI Prompt Engineering on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Adjusting Vocabulary Level” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Prompt Engineering lesson?
Yes. Every AI Prompt Engineering lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Formal vs Informal Tone
- Specifying Your Audience
- Professional Writing Styles
- Adjusting Vocabulary Level