Should AI Dialogue Lead, Support, or Stay Behind the Scenes in Your Game Prototype?
If you’re an indie designer choosing a role for AI dialogue in one playable prototype, start with the player’s task: what should they be able to do that authored lines cannot support well? Make AI the main interaction only when improvising with a character is itself the play. Use it as a supporting layer when fixed gameplay and story remain in charge. Keep it as a creator tool when the player does not need generative conversation at all. Each choice changes what the player can do, what happens when the system fails, and how much work the team must carry.
Start with the player task
“AI dialogue” can describe two very different things: dialogue generated while a player interacts with a character, and AI used by creators to draft material that is later selected and edited. There is also a middle option: a mostly authored game that uses generated dialogue in a limited supporting role. These are different design decisions, not three levels every game needs to climb.
Ubisoft’s [NEO NPC prototype](https://news.ubisoft.com/en-us/article/5qXdxhshJBXoanFZApdG3L/how-ubisofts-new-generative-ai-prototype-changes-the-narrative-for-npcs) explored player-facing, free-form conversation with characters shaped by writers and constrained by scenario and character guidance. Ubisoft describes NEO NPC as a prototype, not evidence that the approach will work in every shipped game. By contrast, [Ghostwriter](https://news.ubisoft.com/en-gb/article/7Cm07zbBGy4Xml6WgYi25d/the-convergence-of-ai-and-creativity-introducing-ghostwriter) generates draft variations of ambient barks for writers to choose and polish. The intended user is the writer; the generated text is not presented as a live player conversation.
A third design reference is entirely authored. In [Strange Scaffold’s account of *Sunshine Shuffle*](https://www.gamedeveloper.com/design/deep-dive-creating-seamless-dialogue-for-sunshine-shuffle), designer Stav Hinenzon describes organizing lines, barks and longer storylets around poker gameplay, with priorities and timing rules to manage which content takes focus. The game’s dialogue system supports the experience without requiring generated text. Together, these examples suggest a practical prototype question: does the player need to improvise with a character, does the game need a responsive layer around authored content, or does the team mainly need help producing drafts?
Compare the three roles
**Main interaction**: Ask or say things in free-form conversation to pursue a goal through a character.. The player’s input changes which useful information, action, or relationship state becomes available. Track task completion and whether responses correctly support the next intended action.. Give the character a defined set of supported topics and state changes; for unsupported input, use a written redirect or return to a known conversation state.. Highest. The team must define character and scenario constraints, connect dialogue to game state, handle unexpected input, and test many more exchanges than a fixed script.
**Supporting layer**: Play the core game while dialogue reacts to selected events or offers optional responses.. A line can clarify, color or respond to an event without blocking the core action. Track whether players notice relevant lines and whether they can continue the task without waiting.. Keep the main path and essential information authored. If generation is unavailable or unsuitable, use a fixed line, a quiet response, or no line.. Medium to high. The team must coordinate generated output with authored beats, timing, character voice and the gameplay states that can trigger it.
**Creator tool**: Play the authored game; a writer or designer uses AI during production to draft variations.. The player receives selected, edited content. The production consequence is whether the tool helps the team produce usable options for a defined writing task.. The writer’s approved script is the fallback and final in-game content.. Variable, but there is no live dialogue system to support. Costs shift to tool setup, review, selection, editing and fitting the process into writing work.
These consequences are design targets to test, not guaranteed effects. Measure only what matters to the prototype’s task. A conversation can be fluent and still fail if it gives no actionable information; a generated bark can be charming and still interrupt a turn the player needs to take.
Choose main interaction when improvisation is the mechanic
Use player-facing free dialogue when the game’s core promise depends on the player expressing an idea the designer cannot conveniently enumerate in advance, and the character’s response can meaningfully affect what the player does next. Decide what counts as a successful exchange before building the model: perhaps the player obtains a clue, gets an NPC to perform one of a few valid actions, or learns a fact through a chosen line of questioning.
Bound the prototype’s world. Define what the character knows, what they will not do, what game state they can change, and what happens when a prompt is irrelevant. Ubisoft’s NEO NPC description emphasizes writer-created character histories and behavior guidance, along with iteration when the model strays from the intended character. That points to a real production task: authors still need to judge whether each response fits the character and scenario. Don’t let improvised text silently become a new plot fact or a game-state command unless you have explicitly designed and tested that path.
A useful fallback is authored and visible in the design: when the input falls outside the supported task, the character can ask a clarifying question, explain what they can help with, or return the player to known options. If the prototype cannot reliably preserve the goal, make the goal’s essential information available through fixed dialogue or another authored route.
Choose a supporting layer when authored beats carry the game
A supporting layer can react to gameplay events while the main interaction remains predictable. This might mean optional ambient remarks, short responses to a small set of actions, or generated variations around a fixed narrative beat. Set boundaries: which events can trigger a line, whether a line can interrupt, and whether its content is allowed to introduce new information.
*Sunshine Shuffle* offers a useful authored comparison: its dialogue units, priorities and timing were designed around poker turns and story pacing. The developer’s account notes that lines needed to leave players time to read and that player input advanced storylet lines. The lesson for a supporting AI layer is concrete: responsiveness has to fit the activity’s timing. A line that arrives during a decision may compete with play even if its wording is good.
Keep authored text for instructions, clues, decisions and any beat whose timing matters. For optional reactions, define a quiet failure mode: skip the line, use a fixed alternative, or defer it until the activity has room. Test whether players can still do the core task with the generated layer turned off.
Choose a creator tool when the player does not need generated speech
If your need is more writing material—especially many short variations—try the creator-tool role first. Ubisoft describes Ghostwriter as proposing bark drafts that scriptwriters can select and polish, with human feedback built into the process. That is a production workflow, not a player-facing NPC interaction. Keep approved lines under the writer’s control and test the tool against a real, bounded drafting task, such as producing alternatives for one character’s reaction to one event.
This choice is especially suitable when your game already uses authored dialogue and variation is useful but not itself a mechanic. The prototype can be played without the tool at runtime. Evaluate the tool by whether its suggestions are usable after review and fit the team’s writing process—not by counting raw generated lines. The source describes Ubisoft’s tool and workflow; it does not establish that the same approach will save time for every team or project.
Run a tiny playtest before expanding scope
Here is an illustrative test, not a result from a published study. Imagine a prototype in which a player must learn a guard’s patrol clue and get through a door. Give a small group of testers one version where the guard accepts free-form questions, one where the game offers authored dialogue choices with optional reactive remarks, and one fully authored version. Keep the goal and clue identical across versions.
For each attempt, record three things: whether the player gets the clue, whether they can open the door, and where they get stuck or wait. Also note whether the conversation changes a decision the player makes. If free-form conversation produces varied wording but no meaningful change in the player’s route or choices, that is weak evidence that it deserves to be the main interaction in this prototype. If an authored path works clearly and the optional reactions do not disrupt it, the supporting role may be enough. If the team’s main difficulty is drafting extra guard barks and the player task is already served, test a creator tool outside the playable build.
These observations can help choose the next prototype iteration; a tiny playtest cannot establish universal performance or predict how a larger audience will respond. Keep the versions small enough that you can compare the player task, the fallback and the work required to maintain each one.
Make the decision with three questions
Before adding AI dialogue, write down answers to these questions:
**What is the player trying to accomplish through dialogue?** State an action or decision, not simply “have a natural conversation.”
**What consequence should a successful exchange have?** Name the information, choice or permitted state change, then decide how you will observe it in a playtest.
**What happens when generation is unavailable or off target?** Provide an authored route that preserves the player’s ability to finish the task.
Then estimate production burden against the size of the prototype: the number of states and triggers, the amount of character and scenario guidance, the review needed for generated material, and the fallback content you must write. A role with a larger player-facing promise also carries more responsibility to keep outputs useful and consistent. For a small prototype, a narrowly scoped test can tell you whether that extra burden serves the player task.
The choice is not a verdict on AI in games. It is a decision about where generative dialogue belongs in this particular playable experience: at the center of what the player does, in a controlled supporting role, or in the creator’s workflow. Pick the smallest role that can demonstrate the consequence you want players to experience, and let the prototype give you a reason to expand it.