AI LLM Runtime Integration

ai-llm-runtime-integration

Overview

Orchestrate large language models at runtime for dynamic NPC dialogue, mission generation, and world-building while maintaining safety guardrails and performance budgets. This skill enables production-grade LLM integration in game engines with fallback strategies and measurable SLOs.

Key Capabilities

1. LLM Service Integration

• Remote APIs: OpenAI, Anthropic, Meta, Azure OpenAI with fallback chaining
• On-Device Models: ONNX Runtime, TensorFlow Lite for offline capability
• Streaming Responses: Token-by-token dialogue generation for real-time character interaction
• Batch Processing: Async mission generation with configurable QoS tiers

2. Safety & Guardrails

• Content Filtering: NSFW, violence, PII detection at ingestion and output
• Token Budget Enforcement: Hard limits on API spend per session/world
• Latency Budgets: Fail-open gracefully when responses exceed SLO (fallback to procedural)
• Rate Limiting: Per-player, per-NPC throttling with queue management
• Jailbreak Detection: Prompt injection mitigation via semantic anomaly scoring

3. Mission & Dialogue Generation

• Context Awareness: World state, player history, NPC personality injection
• Deterministic Seeding: Reproduce missions for testing/replay with fixed seeds
• Branching Narratives: Dynamic mission trees based on player choices
• Localization: Multi-language generation with style preservation

4. Performance & Observability

• Response Caching: LRU cache for repeated generation patterns
• Latency Tracing: End-to-end timing from request to gameplay integration
• Usage Analytics: Token counts, API costs, fallback rates per feature
• A/B Testing: Variant generation for NPC dialogue quality measurement

Implementation Pattern

// Pseudo-code: High-level orchestration
class NPCDialogueGenerator : MonoBehaviour {
    public async Task<DialogueNode> GenerateResponse(
        NPCContext context,
        PlayerInput input,
        CancellationToken ct = default)
    {
        // 1. Load player history + world state
        var memoryContext = await LoadPlayerMemory(context.PlayerId);

        // 2. Build prompt with safety constraints
        var prompt = BuildPrompt(context, memoryContext, input);

        // 3. Orchestrate across providers with fallback
        var response = await LlmOrchestrator.GenerateWithFallback(
            prompt: prompt,
            maxTokens: context.TokenBudget,
            timeout: TimeSpan.FromSeconds(5),
            providers: new[] { "primary", "fallback", "procedural" }
        );

        // 4. Validate & cache result
        if (!await ValidateContent(response)) {
            response = await GenerateFallbackDialogue(context);
        }

        // 5. Record analytics
        await RecordUsage(context, response);

        return ParseDialogueNode(response);
    }
}

Mandates

• Measurable SLOs: Define latency, cost, and fallback rate budgets upfront
• Safety Gates: Content filter + jailbreak detection must run on all responses
• Platform Validation: Test on target hardware with real network conditions
• Privacy Compliance: No PII in logs, GDPR-compliant caching strategies
• Rollback Plan: Graceful degradation to procedural generation under load

Best Practices

1. Budget First: Set hard token/cost limits per play session
2. Fallback Early: Always have deterministic procedural generation as backup
3. Cache Aggressively: Reuse generated content for common scenarios
4. Test Jailbreaks: Red-team your prompts before production
5. Monitor Drift: Track changes in model output quality over time

Risks & Mitigations

Resources

• LLM Integration Best Practices
• Safety Guardrails Checklist
• Cost Optimization Strategies
• Example: examples/npc-dialogue-generator.cs