export default narrative-diction

name	narrative-diction
description	Choose words for register, connotation, and precision. Use when: (1) asked to adjust formality or vocabulary, (2) text contains words like "delve" or "leverage", (3) writing for technical or general audience, (4) translating between casual and formal language, (5) text uses abstract terms without concrete reference.

Diction

Select words that carry the right weight, formality, and associations for the specific context and audience.

Instructions

When I examine word choices, I assess whether each word serves its purpose in context, considering not just dictionary meaning but connotations, formality, and audience expectations.

1. Match register to context - Calibrate formality level to the situation, replacing casual phrasing with structured vocabulary where formality serves the context, or identifying where formal language creates unnecessary distance in contexts calling for directness.

2. Choose words for connotation, not just denotation - Examine each significant word to determine whether its emotional and cultural associations align with intended effect. Words with similar definitions carry vastly different implications: "slim" versus "skinny," "persistent" versus "stubborn."

3. Calibrate vocabulary to audience knowledge - Assess whether technical depth matches what readers know. Replace jargon with plain language for general audiences, use precise technical terms for experts, find middle ground for mixed audiences.

4. Replace habitual patterns with deliberate choices - Identify words appearing through habit rather than intention, particularly AI generation patterns like "delve," "utilize," "leverage," "robust," "seamless." Replace formulaic language with precise, natural alternatives.

5. Prefer precision over approximation - Look for words that are close to intended meaning but not exact. The difference between "annoyed" and "furious," between "suggest" and "insist," between "glance" and "stare" is the difference between communicating clearly and forcing readers to interpret.

6. Test rhythm and flow - Read passages aloud to hear whether word choices create natural rhythm or awkward patterns. Replace words that create stumbling blocks while maintaining meaning, since writing is read with an inner ear.

7. Verify cultural and contextual appropriateness - Consider whether words carry associations specific to contexts, regions, or communities that might not transfer to current audience. Check for idioms that don't translate, metaphors from unfamiliar domains, or terms whose meanings create unintended implications.

Examples

From casual to professional register in business communication

I'm drafting an email to a potential client considering our services. My first version reads: "Hey there! Just wanted to reach out and see if you're still into the idea of working together. We could totally make this happen if you're down for it. Let me know what you think!"

Reading this back, I notice the register is too casual for the relationship and stakes involved. "Hey there" signals familiarity we haven't established. "Into the idea" and "if you're down for it" are conversational phrases that don't match the professional context. "Totally" and "just wanted" are verbal fillers that weaken rather than strengthen.

I revise: "I'm writing to follow up on our conversation about collaborating on your project. Based on what you've shared about your goals, I believe we can deliver the outcomes you're looking for. I'd welcome the opportunity to discuss next steps at your convenience."

The transformation replaces casual greetings with professional openings, vague expressions with specific references to previous conversation, enthusiasm markers with confident capability statements. The register now matches the context where a business relationship is being established rather than a friendship being maintained.

From generic to evocative in narrative writing

I'm working on a screenplay scene where a character discovers their apartment has been searched. My initial description reads: "She opened the door and saw that things had been moved. Everything looked different. She felt upset."

These words communicate basic facts but don't create the experience. "Things" is too vague. "Moved" and "different" don't capture the specific disruption. "Upset" is a placeholder emotion that could mean anything from mildly annoyed to devastated.

I revise, choosing words for precise connotation: "She opened the door to wrongness. Drawers gaped. Papers that should have been squared in stacks fanned across the desk. The couch cushions sat slightly askew, as if someone had replaced them quickly, carelessly. Her chest tightened."

Now "wrongness" captures the immediate visceral sense that something is off. "Gaped" suggests violation in a way "were open" doesn't. "Fanned" and "askew" are specific observations that show rather than tell. "Chest tightened" grounds fear in physical sensation rather than naming the emotion. Each word now carries weight specific to this moment. The difference is between reporting that something happened and creating the texture of the experience.

From jargon overload to accessible explanation in technical writing

I'm writing documentation for a data visualization library that will be read by both experienced developers and designers learning to code. My first draft reads: "To instantiate the chart object, invoke the constructor with a configuration hash containing the requisite parameters for the visualization you wish to render. The API leverages a fluent interface pattern enabling method chaining for streamlined declaration of chart properties."

This is technically accurate but assumes expertise with programming vocabulary. "Instantiate," "invoke," "configuration hash," "requisite," "leverages," "fluent interface pattern," and "method chaining" are all jargon that creates barriers for designers.

I revise: "To create a chart, call the chart function with an object describing what you want to display. The library uses chainable methods, so you can set multiple properties in sequence like this: chart.data(myData).width(500).height(300)."

I've replaced "instantiate" with "create," "invoke the constructor" with "call the function," "configuration hash" with "object describing what you want," and explained "fluent interface pattern" by showing what it looks like rather than naming it. The code example demonstrates method chaining without requiring readers to know that term. The revision maintains technical accuracy while choosing vocabulary that serves readers at different expertise levels.

From AI patterns to natural expression

I'm reviewing a draft where I notice phrases that signal artificial generation: "It's important to note that this approach leverages existing infrastructure to seamlessly integrate with your workflow, enabling you to efficiently optimize outcomes in a robust manner."

Reading this, I see multiple habitual AI patterns stacked together. "It's important to note that" adds no information and delays the actual point. "Leverages" is AI-speak for "uses." "Seamlessly integrate" is a cliche that promises friction-free implementation but rarely describes reality accurately. "Enabling you to efficiently optimize" uses three abstract process words where one concrete action would suffice. "In a robust manner" is filler that sounds technical while meaning nothing specific.

I strip these patterns and rebuild with intention: "This approach uses your existing infrastructure. It works with your current workflow without requiring changes to how your team operates. You'll see results faster."

The revision eliminates AI markers by choosing specific verbs over abstract process language, concrete outcomes over vague optimizations, and direct statements over ceremonial framing. Each word now does work rather than filling space.