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To make an LLM-powered conversational game, let the model interpret player input and produce dialogue or a limited action proposal, while your game application keeps the authoritative world state and decides which changes actually happen. A basic turn loop is: provide the current scene and relevant game state, receive a response, validate any proposed action, update the state in code, then show narration that reflects the result.
Choose how much control the model has
There are three useful starting points. They differ in player freedom, author control, and the work required to keep play consistent.
| Pattern | What the model does | Trade-off |
|---|---|---|
| Prompt-only conversational play | Narrates and responds to player moves in a chat or text interface. | Quick to prototype, but rules and memory can drift unless the application supplies canonical state and checks consequential claims. |
| Schema-constrained mechanics | Returns dialogue plus defined fields, such as an action category, acceptance flag, target, or numeric result. | Supports controlled game mechanics, but the application must validate fields and apply only permitted consequences. |
| Open-ended action generation | Helps create rules or effects for player-invented actions grounded in a represented world state. | Allows broader player freedom, but requires more engine support to represent and validate preconditions and effects. |
Epic’s UEFN documentation illustrates the schema-constrained approach: its vehicle-barter example returns whether an offer is accepted and an agreed price, while Verse code processes the result, awards the player, updates score, and removes the NPC. The application performs the actual game changes rather than treating dialogue as proof that an outcome occurred. See Epic’s LLM game mechanics example.
For open-ended actions, the STORY2GAME paper describes grounding generated actions in a game engine’s state representation through preconditions and effects. The paper also discusses generating actions for player-invented moves; doing this can require changes to the state model and revisions to existing actions. That flexibility comes with more engine complexity than a fixed action schema. Read STORY2GAME: Generating (Almost) Everything in an Interactive Fiction Game.
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Keep game state authoritative in your application
Decide what the application, not the model, owns. A compact state might include the current scene, character status, inventory, score, and completed objectives. Send only the parts relevant to the current turn, together with the scene and rules the model needs.
Have the model propose an action in a defined format; let application code check that the action is allowed, that its parameters are valid, and that its preconditions hold. Then update state in code and generate narration from the resulting state. This prevents a sentence such as “You found the key” from silently changing inventory when the game has not granted a key.
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Build the first playable loop
- Set the rules. Write down the turn structure, win and loss conditions, and the actions players may take.
- Define canonical state. Choose the fields the application will track, such as scene, character status, inventory, score, and completed objectives.
- Prepare each turn’s context. Send the current scene and only the relevant state, rather than relying on the model to remember prior events correctly.
- Specify the model’s job. Define its character voice, knowledge, applicable rules, permitted response fields or actions, and how it should respond to unknown or impossible requests.
- Validate proposed actions. Check action names, parameter types and values, and game-rule preconditions in application code. Apply consequences there.
- Narrate the applied outcome. Build the player-facing response from the updated state so the story text and mechanics agree.
- Test edge cases. Try ambiguous requests, repeated actions, invalid values, attempts to bypass rules, and situations where earlier context is missing.
For a first build, a small set of typed fields or registered actions is usually easier to control than dynamic action generation. Expand toward player-invented actions only when the engine can represent and validate their preconditions and effects.
Design the prompt and narrative structure
A character prompt should distinguish roleplay from authority. Specify the character’s identity and voice, what the character knows, the current scene, the rules it must honor, and the information the game needs back. Keep authoritative facts and legal outcomes explicit; prose instructions alone should not decide whether a consequential action succeeds.
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Epic’s UEFN Conversations template combines persona instructions with structured output and routes dialogue to a UI manager for closed captions. Epic’s structured-output gameplay documentation lists Verse-supported field types as int, float, bool, enum, and message, and shows an action and handler routing a structured response into gameplay. These are platform-specific details; check Epic’s live documentation for current support.
For a larger authored story, separate style, characters, scenes, opening lines, and conditional events. The Dramamancer design paper describes an author-facing schema with these components; events can define conditions and outcomes that transition between scenes or end one. This gives authors narrative boundaries while leaving the model room to realize moment-to-moment dialogue. See Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System.
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Use structured responses and tool calls carefully
When a game depends on predictable fields such as accepted or price, schema-constrained output can make the response easier to process. OpenAI distinguishes Structured Outputs, which adhere to a supplied schema, from JSON mode, which produces valid JSON but does not itself guarantee that the output matches a schema. A schema still does not establish that a proposed action is legal in your game: handle refusals, failures, and out-of-scope or invalid decisions in application code. Consult OpenAI’s Structured Outputs guide.
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Tool calling is another way to connect model decisions to game functions. OpenAI describes it as an application-controlled sequence: provide available tools, receive a tool request, execute the requested tool in application code, return its result, and receive a final response or another tool request. Expose only actions the game can safely execute, and validate arguments before changing state. See OpenAI’s function-calling guide.
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Choose the right amount of player freedom
- Favor a fixed schema when actions affect score, inventory, objectives, or other mechanics that must stay predictable.
- Favor prompt-only play when the goal is a lightweight conversational prototype and the consequences of narrative drift are limited.
- Consider open-ended actions when player creativity is central and the game engine can represent the world conditions and effects needed to validate new actions.
The cited systems show workable patterns, not a universally best approach. Your choice depends on how much freedom you want to offer and how much consistency and author control the game requires.
Quick Recap
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