ElevenLabs pronunciation dictionary: fix names before generating narration

A practical workflow for building pronunciation notes before generating AI narration.

The fastest way to get a useful result from ElevenLabs is to decide what the work is supposed to become before you ask the model to help. In this guide, the output is a pronunciation dictionary for voiceover scripts. The audience is creators producing narration with product names and technical terms. That sounds obvious, but it prevents the most common failure: the voice sounds good until it mispronounces one product name, acronym, founder name, or technical term in the middle of the final take.

This tutorial uses a small editorial workflow rather than a giant prompt. You will write the brief, prepare inputs, run the model, review the result, and save the reusable parts for next time. The example is a tutorial narration that includes NotebookLM, LoRA, JSON, API, and several brand names.

What you will build

You will build a repeatable workspace with three parts:

• A short brief that defines the goal and audience
• A working prompt or checklist that guides ElevenLabs
• A review pass that catches weak output before it becomes published work

The goal is not to automate judgment. The goal is to remove avoidable mess so your judgment can focus on the parts that matter.

Step 1 - write the working brief

Start with a four-line brief. Do this before opening ElevenLabs.

Goal: a pronunciation dictionary for voiceover scripts
Audience: creators producing narration with product names and technical terms
Example: a tutorial narration that includes NotebookLM, LoRA, JSON, API, and several brand names
Must avoid: testing pronunciation only in the final file

A brief like this keeps the session grounded. If the first output is wrong, you can point to the line that failed. If the output is surprisingly good, you can reuse the same structure later.

Step 2 - prepare the inputs

Good AI work usually fails because the inputs are messy. Before prompting, collect only the material that belongs in this task. Remove private details, duplicate examples, old notes that no longer apply, and anything you are not willing to verify later.

For this workflow, prepare:

• One clear source or example
• One description of the desired output
• One list of constraints
• One list of things the model should not invent

Step 3 - run a narrow first pass

Use ElevenLabs for a first pass that is intentionally narrow. Ask it to produce the structure before asking for the final result.

Using the brief below, create a first-pass structure for a pronunciation dictionary for voiceover scripts.
Do not polish yet.
Flag missing information instead of guessing.
Keep the output practical and easy to review.
 
Brief:
[Paste the four-line brief here]

This prompt is not glamorous. That is the point. A rough structure is easier to fix than a polished wrong answer.

Step 4 - review with a checklist

Review the first pass against a checklist, not your mood. For this workflow, check:

• scan the script for names
• write phonetic versions
• generate a short test line
• listen on speakers
• keep the dictionary beside the script

If two or more items fail, do not revise sentence by sentence. Rewrite the brief. A bad brief creates bad revisions.

Step 5 - revise one variable at a time

When you revise, change one thing per pass. For example, ask for clearer structure, then ask for better wording, then ask for final cleanup. If you change tone, format, length, and examples at once, you will not know which change helped.

A useful revision prompt:

Revise the last output against this checklist.
Preserve the parts that already work.
Do not add new facts.
If a checklist item cannot be satisfied, explain why.

This keeps ElevenLabs from turning a focused task into a new draft with new problems.

Step 6 - save the reusable pattern

After the output is good, save the pattern, not just the result. Keep the brief, the prompt, the checklist, and one note about what failed. The failure note is valuable because it prevents you from repeating the same weak direction next week.

Save it like this:

Workflow: ElevenLabs pronunciation dictionary: fix names before generating narration
Best prompt: [paste final prompt]
Checklist: [paste review checklist]
Failure note: [what produced weak output]
Reusable next time: [what should stay]

Common mistakes

Avoid these traps:

• testing pronunciation only in the final file
• assuming acronyms are obvious
• forgetting regional names
• fixing one line while leaving the same word elsewhere

The pattern behind all of them is the same: asking the tool to make too many editorial decisions at once. Keep the model focused, then make the final decision yourself.

Final checklist

Before publishing or sharing the output, confirm:

• The original goal is still visible in the final result.
• The output fits the intended audience.
• Any factual claim can be traced to a source or input.
• The result has been reviewed in the format where it will actually be used.
• The reusable prompt and failure note are saved.

FAQ

Should I write phonetics for common words?

Only for words the voice gets wrong or words that must be pronounced a specific way.

How do I test a pronunciation?

Generate one short sentence with the word in context and listen before producing the full script.

Should acronyms be spaced out?

Often yes. Writing A P I can be clearer than API, depending on the voice.

What if the tool ignores my spelling?

Try a phonetic rewrite in the script line itself and regenerate only that section.

Should I keep a dictionary per project?

Yes. A product or channel dictionary saves time across future narrations.