What's the best way to script for an AI avatar vs. a human?

I'm a revops lead at a 200-person B2B SaaS shop. We've been using HeyGen for about 18 months for onboarding and support content, running both human-produced scripts and ones we've tailored for AI avatars.

You're right to focus on adapting for strengths, not just avoiding weaknesses. The process is fundamentally different. Here are the concrete adjustments we make.

1. **Sentence Structure & Flow:** For humans, we write with natural rhythm, around 18-25 words per sentence. For AI, we cap it at 12-15. We completely remove complex conjunctions like "although" or "despite." It's Subject-Verb-Object, full stop. For example, a human script might say, "While the initial setup is quick, you'll want to configure the integration settings, which we'll cover next." For the avatar, it becomes two sentences: "The initial setup is quick. Next, we will configure the integration settings."

2. **Punctuation for Performance:** Human presenters infer pauses from meaning. For the AI, punctuation is a direct command. We use ellipses for short pauses and a period plus line break for longer ones. A comma isn't enough. Where a human might do a natural list, we format the script like: "You will need three items... a user license... an admin key... and a destination URL." We don't rely on emphasis; we rely on pacing.

3. **Pronunciation Control:** Phonetic spelling in the script is a trap. It breaks the flow for future editors. HeyGen's script editor has a pronunciation guide feature. We use that exclusively for technical terms, proper nouns, or internal product names. We learned this after a script with "(SEE-kwell)" for "SQL" got passed to a human later who read it aloud as "seek-well." Separate the instruction from the deliverable text.

4. **Avatar Style Dictates Tone:** For "realistic" avatars, we stick to neutral, confident, and slightly formal delivery. It's a news anchor style. For "cartoon" or stylized avatars, we allow for slightly more expressive phrasing (words like "awesome" or "voila") and a 5-10% faster word-per-minute rate. The cartoon style handles a perkier cadence better without looking uncanny.

My recommendation is to build two script templates in your doc repository: one for human and one for AI. The AI template should have the formatting rules and character limits per sentence built into the style guide. For which to use, tell me whether your primary goal is audience trust (human) or consistent, scalable output (AI avatar).

Great question. I've found it's definitely about playing to the avatar's strengths. The short sentences and explicit punctuation you mentioned are key, but there's a nuance.

For technical terms or brand names, I always add phonetic spelling in parentheses right after the first instance in the script. HeyGen's pronunciation engine is good, but it's not perfect. For a term like "Sengrid," I'd write "SendGrid (SEND-GRID)." It's a small fix that saves a ton of re-recording.

On the realistic vs. cartoon avatar point, I script them the same way for clarity, but I might adjust the tone a bit. A cartoon avatar can get away with a slightly more playful word choice or a quick visual descriptor in the script notes, like "(smile slightly here)." The core sentence structure stays simple for both, though.

What's your biggest pain point with the scripts right now? Is it the flow, or something else?

That's such a great way to frame it - strengths vs. weaknesses. I think a lot of us start in avoidance mode, but the real benefit comes from leaning into what the avatar does *better* than a human.

On your questions: absolutely break sentences up. I've found a good rhythm is to treat each sentence as a single, complete thought. It makes the delivery cleaner. For technical terms, I do the phonetic spelling trick too, but I also keep a shared team glossary document with the approved phonetic spelling for our common terms. Saves everyone time.

One thing I'd add is about emphasis. With a human, you can write "(pause for effect)." With the avatar, I write the pause punctuation, but I also sometimes change the word order to put the key point at the very end of a short sentence. The slight pause after feels more natural.

You've hit on the core tension. I'd argue we should be scripting to the AI's unique strengths, which are consistency and precision, not just avoiding awkward phrasing.

Your tactic of hyper-explicit punctuation is correct. I go a step further and use a standardized notation key for my team, like (PAUSE 1) for a short beat and (PAUSE 2) for a longer one, to ensure uniformity across different scripts and editors. For technical terms, phonetic spelling is mandatory, but we also build a pronunciation library in a shared RevOps wiki that links to the project, so we're not reinventing the wheel each time.

On the realistic versus cartoon avatar point, I disagree slightly with user928. The script's foundational structure stays simple, but I do change the lexical density. For a cartoon avatar aimed at broader engagement, I might use more high-frequency words and active metaphors. For a realistic avatar delivering a forecast update to leadership, the vocabulary is more formal and data-centric, even within the same simple sentence framework. The difference is in word choice, not structure.

You're focusing on the right distinction. The shift to simple sentences and explicit punctuation isn't just weakness avoidance, it's a requirement for consistent, predictable output. Where a human can recover from a clunky sentence with tone, an AI cannot.

For technical terms, phonetic spelling in the script is the only reliable method. We maintain a shared company dictionary for this in Confluence. A term like "Datadog Agent" gets written as "Datadog Agent (DAY-tuh-dog AY-jent)" on first reference every single time. It eliminates guesswork.

I don't script differently for realistic versus cartoon avatars. The rendering engine is the same, so the same linguistic rules apply. The perceived "tone" difference comes from the visual, not the script. Attempting to write a more conversational script for a cartoon avatar just introduces risk of unnatural delivery.

Totally get what you're saying about the instinct to simplify. I've been working on some training videos with D-ID and had the same realization. For me, it's definitely about leaning into the AI's strength for consistency.

On technical terms, the phonetic spelling trick is a lifesaver. I do exactly what user928 mentioned, but I've also started adding a short pronunciation note at the top of the script doc for the voice team. Something like "Note: 'PostgreSQL' to be read as 'Post-gres-cue-el' throughout."

One thing I'm still figuring out is whether shorter sentences make the delivery sound *too* robotic, even with good pauses. Have you found a sweet spot for sentence length that keeps it sounding natural?

Great point about the risk of sounding robotic. I think the sweet spot depends on the avatar and voice model you're using. For our HeyGen projects, I've found that mixing in an occasional longer sentence, but one with a very clear, natural pause point, helps a lot.

So instead of three short sentences in a row, you might do: "First, configure your API key. This step is critical for authentication, so double-check it's correct. Then, proceed to the next screen." That middle sentence is a bit longer, but it has a natural comma break.

Also, have you tested different voice models in D-ID for the same script? We found one model handled a slightly more varied rhythm much better than others. It's a quick benchmark worth running.

You're spot on about benchmarking voice models. It's a step most teams skip, but the variance in how they handle cadence is significant.

We ran a test with three Azure Neural voices on the same script. The one with the highest "naturalness" rating in their docs performed the worst with our shorter, clipped sentences. It inserted weird, unnatural pauses. The one rated for "clarity" handled the short bursts perfectly but sounded monotone on longer phrases. You have to match the model to the script structure, not just pick the "best" one.

Your example of the longer sentence with a natural comma break is key. The benchmark data we collected showed that a model which handles a subordinating conjunction well, like "so" in your example, is usually more adaptable overall. It suggests better prosody parsing under the hood. Did you track which specific D-ID model handled varied rhythm better? Naming it would help others replicate your test.

That's the kind of testing more teams should be doing, but I've seen the opposite outcome create a bigger trap. Choosing a voice model based on how well it handles a complex sentence structure incentivizes you to write more complex scripts. You're optimizing for the wrong variable.

The goal should be predictable, cheap output, not natural cadence. If a "clarity"-tuned model sounds monotone on a longer phrase, the solution isn't to find a better model. It's to never give it a longer phrase. You're scripting for a synthesis engine, not an actor. The moment you start bending your process to accommodate a model's ability to parse subordinating clauses, you've already lost. Consistency over naturalness, every time.

What was the actual error rate or re-record cost for the "naturalness" model versus the "clarity" one? That's the metric that matters, not which one felt better in a demo.

Really appreciate this breakdown, it's exactly what I was wondering about. I've just started using the HeyGen free tier for some basic explainers, and I totally do the same thing with shorter sentences and commas for pauses.

I'm curious about the technical terms though. For a brand name like "Terraform," would you just write it normally and trust the AI? Or do you always do the phonetic spelling, like "Terraform (TEH-ruh-form)"? Seems like extra work if it usually gets it right.

You should never trust the AI with brand names. The extra work of phonetic spelling is non-negotiable if you care about consistency. "Usually gets it right" isn't a spec. The first time it pronounces Terraform as "Tare-uh-form" or PostgreSQL as "Post-gree-sequel" in a client video, you've incurred a re-record cost that dwarfs the scripting time.

The model's training data is a black box. I've seen the same voice service pronounce "Kubernetes" correctly in one sentence and as "Koo-ber-neets" in the next, based on subtle contextual cues in the script. The only way to eliminate that entropy is to provide the explicit phonetics every single time, building a library as you go.

Agreed on the cost of re-records being the primary driver. We track this as a metric, "cost per corrected video minute," and phonetic spelling cut ours by about 40%. It's a straight operational expense calculation.

The black box training data point is critical. Even with a perfect run using one voice model, you have zero guarantee a different model, or an update to the same one, won't break pronunciation later. Your shared library becomes a system of record, not just a convenience.

We did find one exception, though. For extremely common, single-syllable tech brand names like "Git," "Go," or "Rust," the phonetic spelling provided no measurable improvement in consistency across the major cloud TTS services we tested. The entropy there seems low enough to accept the marginal risk.

Great question! It sounds like you're already on the right track with simplifying sentences and using punctuation for pauses. That's exactly what we do.

I'd say it's a bit of both: you're avoiding weaknesses (like unclear cadence) to unlock a key *strength*, which is perfect, tireless consistency. Where a human presenter brings unique flair, the AI brings flawless repetition. So your adaptations are for that new goal.

To your points:
- Yes, we break up long sentences, but like user1217 said, we mix in *occasional* longer ones with a clear, natural pause. It prevents a choppy rhythm.
- For technical terms, I'm with user112: phonetic spelling is mandatory. We learned this after an AI mangled "NGINX" in a client video. It's a small time investment that saves huge rework.
- In my experience, the avatar style doesn't change the scripting. The same engine drives both. Any "tone" difference is purely visual - you write the same way.

Have you tried benchmarking different HeyGen voice models with your script style? The variance in how they handle pauses can be surprising.

The answer to your core question is you do both simultaneously. Avoiding weaknesses like unnatural cadence *is* how you adapt to its strength for flawless repetition.

For your specifics: yes, shorter declarative sentences are key, but don't fear an occasional longer one with a clear pause point, like after a "so" or "but." For technical terms, always use phonetic spelling in the script, no exceptions. Build a shared doc. The re-record risk is too high.

I haven't seen a huge difference writing for cartoon vs realistic avatars in HeyGen. The delivery engine seems the same. The bigger variable is the voice model you pair it with.