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Make the game engine—not the language model—the authority over what the pet can do. Give the model a small, typed set of possible actions, constrain its output where your local runtime supports it, and validate every proposal against the live game state before execution. This can prevent an invented command from changing the game, even though it cannot guarantee the model will never propose one.
Why valid JSON is not enough
A response can be well-formed JSON and still ask for an action that does not exist, use an invalid argument, target an unavailable item, or refer to an old turn. Schema-constrained generation can limit output to a defined structure; it does not, by itself, establish that an action is legal in the current game.
OpenAI describes constrained decoding as restricting generation to tokens that remain valid under a supplied schema. That is a description of the mechanism, not proof that every local runtime implements it: OpenAI’s explanation of Structured Outputs. Your application still needs to check the proposal against authoritative game state before acting.
Build an action pipeline with a deterministic execution gate
Use the model as a bounded proposal generator or narrator. Keep canonical state, action execution, scoring, transitions, and outcomes in ordinary game code. A practical loop is:
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observe current state → provide legal actions and relevant state → generate a constrained proposal → validate against current state → execute or reject → report the result
- Define the action contract. Choose a short list of action names, such as
feed,play, andsleep. Give each action typed arguments, and use an enum when an option comes from a genuinely finite set. Keep conversational prose out of the execution fields. - Include freshness information. Add a turn, round, or state identifier when a proposal could be applied after the game has moved on. Compare it with the current identifier at execution time.
- Constrain generation where supported. Use a JSON Schema, grammar, or strict tool arguments to limit the response shape and, where possible, its value space. Verify support for the exact model and serving configuration rather than assuming a prompt instruction such as “return JSON” enforces a schema.
- Validate at the game boundary. Parse the result; check that the action exists, its arguments are valid, its target is available, and the action is legal in the current state. Execute only after all checks pass.
- Reject rather than guess. Treat malformed output and invalid proposals as failures. Do not silently translate an unknown action into something else. If you request a retry, validate the new proposal through the same gate.
- Record and evaluate behavior. Log the state identifier, allowed actions, raw response, parsed proposal, validation result, and executed action. Test valid actions, unknown actions, malformed arguments, ambiguous requests, stale turns, unavailable targets, and requests outside the action set. Measure rejection and task-success rates before making reliability claims.
Separate command turns from conversation
For a game command, ask for the structured action contract and nothing that could be mistaken for an executable instruction. For ordinary pet conversation, use the normal response path. This keeps narration distinct from actions and makes it easier to decide what the game may execute.
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Tool-call envelopes and schema enforcement vary by runtime. Confirm that strictness applies to the selected tool and its arguments in your deployed configuration; a model’s ability to produce tool-like text is not the same as the server enforcing a schema.
Choose constraints your local runtime actually supports
| Approach or runtime | What it can do | Qualification |
|---|---|---|
| Prompt-only instructions | Tell the model which actions and formats are expected. | An instruction is not an execution gate; validate proposals in application code. |
| LocalAI grammar parameter | Constrain output with a BNF grammar. | LocalAI documents this feature for its llama.cpp backend only. Do not assume the same support on other backends: LocalAI constrained grammars documentation. |
| llama.cpp schema-to-grammar parsing | Convert JSON Schema into a grammar for constrained output. | The parser documentation notes that some generated grammars may be ambiguous and describes fallback behavior for a pattern involving .*. Keep schemas simple and test the exact schema/runtime combination: llama.cpp JSON Schema-to-grammar documentation. |
| Application-side validation | Check the parsed proposal against current game state and permissions before execution. | This is the execution gate, regardless of whether generation was constrained. |
Validate legality against live state
A static schema might restrict action to feed, play, or sleep. It cannot establish that the pet is hungry, that the toy is available, or that the request still belongs to the current turn unless those conditions are represented and checked against authoritative state. A documented minigame design illustrates this distinction: it rejects a stale round or a choice absent from that round, while computing results deterministically in game code rather than letting an agent choose the winner: Paradise Hearts minigame system documentation.
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Return a compact error to the model or interface when validation fails—for example, invalid_action, argument_out_of_range, target_unavailable, or stale_turn. Report what failed without automatically executing a guessed alternative.
Keep outcomes under game-code control
The model may suggest an action or describe what happens in character, but the game should decide whether the action is allowed and compute its consequences. Do not let generated prose change hidden facts, scores, or relationships. The Paradise Hearts documentation is an example of this separation, not a benchmark for desktop pets or a universal standard.
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Account for compatibility and latency
Grammar and schema support depends on the serving stack, backend, and model. LocalAI’s documented llama.cpp-only boundary is specific to its grammar parameter; it should not be generalized to other LocalAI backends or local servers.
OpenAI’s article describes preprocessing a schema, with latency on the first request for a new schema and caching for later sampling. That is an API mechanism description, not a local-runtime latency measurement. If response time matters, benchmark your actual model, schema, and serving configuration.
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What you can claim about reliability
This architecture gives the game a deterministic place to reject invalid proposals. It does not establish that a particular desktop pet will never receive an invented action, nor does the cited material report a desktop-pet success rate. Evaluate your own setup with recorded test cases and report measured results only for the model, runtime, and conditions you tested.
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