To make an LLM-powered conversational game, let the model interpret player messages and produce dialogue or a tightly defined action proposal, while your game code owns the authoritative world state. Validate each proposed action in the application before changing inventory, score, characters, or objectives. This division lets players speak naturally without letting a generated sentence rewrite the rules.
Choose how much control the model gets
There are three useful starting patterns. They trade player freedom against author control and the effort needed to keep the game consistent; no single pattern is best for every project.
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| Pattern | How it works | Trade-off |
|---|---|---|
| Prompt-only conversational play | The model narrates and responds to player moves in a chat or text interface. | Fast to prototype, but rules and remembered facts can drift unless the application supplies canonical state and checks consequential claims. |
| Schema-constrained mechanics | The model returns dialogue plus defined fields, such as an action type, target, acceptance flag, or proposed number. Application code validates them and applies permitted effects. | Offers a clear validation boundary and author control, but limits actions to what the game has defined. |
| Open-ended action generation | The game lets players attempt actions beyond a fixed list and grounds generated actions in represented world state, including their preconditions and effects. | Allows broader improvisation but requires a richer state model and more work to validate actions and keep existing rules coherent. |
For a first build, schema-constrained mechanics are a practical middle ground: use the LLM for conversation and a small set of typed fields or registered actions for game effects. Add dynamically generated actions only when the engine can represent and validate their preconditions and effects. The STORY2GAME paper describes this more open-ended approach and the additional state-model work it can require.
Build the game loop around authoritative state
The application—not the model’s prose—should be the source of truth for facts that affect play. A minimal turn can follow this sequence:
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- Receive the player’s message. Keep the original input available for interpretation.
- Send relevant context. Include the current scene, applicable rules, and only the state needed to respond.
- Request dialogue and, if needed, a structured action proposal. Define the permitted fields or actions for this interaction.
- Validate the proposal in application code. Check its type, allowed values, target, and whether the action is legal in the current state.
- Apply the approved change. Update inventory, score, character status, objectives, or other canonical data in the game.
- Present the result. Generate or select narration that reflects the state after the application has processed the action.
If the proposal fails validation, reject it or ask the model for a permitted alternative; do not apply a consequential change just because it appeared in dialogue. This prevents an NPC saying “I give you the key” from granting a key unless the game rules actually authorize that outcome.
Define the rules and state before writing prompts
Write down what counts as a turn
Start with a small loop and explicit win, loss, and turn rules. Decide what ends an interaction, what advances the scene, and which outcomes require an application-side check. If these rules exist only in character prose, later dialogue can contradict them.
Choose a compact canonical state
Track the data the game needs to decide what can happen. Depending on the game, that may include the current scene, character status, inventory, score, and completed objectives. Provide the model with the relevant slice for the current turn instead of relying on it to remember facts from earlier conversation.
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A character prompt can specify identity, voice, what the character knows, the current scene, rules the character must honor, and the information the game needs back. Keep legal outcomes and authoritative facts explicit. A persona helps shape dialogue; it is not a substitute for application-side rules and validation.
Epic’s UEFN Conversations template illustrates this combination: persona instructions shape character responses, structured output supports decisions, and a UI manager presents dialogue as closed captions.
Return structured actions, not gameplay-changing prose
When a conversation affects mechanics, ask for defined output rather than trying to infer game changes from free-form dialogue. For example, a barter interaction could return whether an offer was accepted and an agreed price. Your application can check whether the price is allowed and whether the player can afford it before completing the trade.
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Epic’s LLM Game Mechanics with Verse example demonstrates that boundary in UEFN: a Verse binding processes the structured result, awards the player, updates the score, and removes the NPC after the interaction. The model contributes to the decision, while Verse carries out the gameplay changes.
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In Epic’s UEFN structured-output documentation, the supported Verse field types are int, float, bool, enum, and message. The example registers an action and handler to route the model’s structured response into gameplay. These details are specific to the documented platform and may change; check the live documentation when implementing against it.
Use schema adherence when fields must be predictable
OpenAI distinguishes Structured Outputs, which can adhere to a supplied schema, from JSON mode, which produces valid JSON but does not by itself guarantee that the response follows a particular schema. A schema is useful when code depends on fields such as accepted or price, but it does not make every proposed action legal. Your code still needs to handle refusals, failures, invalid values, and out-of-scope decisions.
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Use tool calls as requests for application work
With tool calling, the model can request an action, but the application executes it. OpenAI describes the flow as a multi-step loop: provide the available tools, receive a tool request, execute the requested tool in application code, return its result, then receive a final response or another tool request. For a game, expose only actions the application can safely perform and validate their arguments before updating state. See the function calling guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Give authors narrative boundaries without scripting every line
For a larger story, define narrative ingredients separately: style, characters, scenes, opening lines, and events. Represent events as conditions and outcomes, including whether they transition to another scene or end one. This gives the model room to realize moment-to-moment dialogue while the author retains control over what can happen and when.
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Test where conversation and rules collide
Once the ordinary turn works, test inputs that can expose mismatches between what the player asks, what the model proposes, and what the game permits:
- Ambiguous requests that could refer to multiple characters, items, or actions.
- Repeated actions, such as trying to collect the same item twice.
- Invalid values or targets that are not available in the current scene.
- Requests that attempt to bypass a cost, requirement, or other game rule.
- Context loss, including a turn where the model lacks a fact needed to answer correctly.
- Refusals, API failures, malformed results, or outputs that cannot be applied to the current state.
For each case, verify that the application preserves its state rules and that the player receives a coherent response. Do not treat a plausible-sounding explanation as evidence that a proposed action was valid.
Quick Recap
References
- Epic Developer Community: Conversations Template in Fortnite
- Epic Developer Community: LLM Game Mechanics with Verse in Fortnite
- Epic Developer Community: Drive Gameplay with Structured Output in Unreal Editor for Fortnite
- OpenAI: Structured model outputs
- OpenAI: Function calling
- STORY2GAME: Generating (Almost) Everything in an Interactive Fiction Game
- Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System
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