Integration Guides
Learn how to integrate AletheionGuard into your production systems with best practices and real-world patterns.
What You'll Learn
These guides cover advanced integration patterns, production deployment strategies, and best practices for building trustworthy AI systems with AletheionGuard.
Topics Covered
- • RAG integration with adaptive retrieval
- • Multi-model comparison and consensus
- • Enterprise deployment architectures
- • Security and compliance
Best For
- • Production deployments
- • Enterprise integrations
- • Complex AI workflows
- • Mission-critical applications
Available Guides
RAG Integration
POPULARBuild intelligent RAG systems that adapt retrieval based on epistemic uncertainty. Learn how to use Q2 scores to trigger re-retrieval and improve answer quality.
Model Comparison
ADVANCEDCompare multiple LLMs and route requests based on uncertainty, cost, and performance. Build consensus systems and intelligent model selectors.
Enterprise Deployment
ESSENTIALDeploy AletheionGuard at scale with Docker, Kubernetes, high availability, monitoring, and enterprise security features.
Real-Time Streaming
COMING SOONMonitor LLM responses in real-time with streaming uncertainty detection. Get incremental confidence scores as text is generated.
Common Use Case Patterns
Customer Support Chatbots
Use epistemic uncertainty to detect when your chatbot doesn't know the answer and should escalate to a human agent.
Content Generation
Validate generated content before publishing. Flag high-uncertainty statements for human review to prevent hallucinations from reaching users.
Knowledge Base Q&A
Build trustworthy Q&A systems that adapt retrieval strategies based on confidence scores and provide citations when uncertain.
AI Agents & Workflows
Monitor multi-step agent workflows and detect when agents need more context or are about to make unreliable decisions.
Integration Best Practices
1. Set Appropriate Thresholds
Start with default thresholds (MAYBE: 0.70, ACCEPT: 0.85) and adjust based on your use case. Critical applications may need higher thresholds.
2. Monitor Q1 vs Q2
High Q2 (epistemic) means the model lacks knowledge—add more training data or context. High Q1 (aleatoric) is inherent uncertainty that cannot be reduced.
3. Implement Graceful Degradation
When confidence is low, fall back to safer alternatives: retrieve more context, consult multiple models, or escalate to humans.
4. Track Metrics Over Time
Monitor average Q1, Q2, and verdict distributions to understand model behavior and identify opportunities for improvement.
5. Cache Audit Results
For frequently repeated queries, cache audit results to reduce latency and costs. Use Redis or similar caching layers.
6. Test with Real Data
Always test with production-like data. Uncertainty patterns may differ significantly between synthetic and real-world queries.
Performance Optimization
Tips for optimizing AletheionGuard integration in production systems:
Batch Processing
Use the batch API endpoint when processing multiple items. Reduces overhead and improves throughput by up to 5x.
POST /v1/audit/batchAsync Integration
Make audit requests asynchronously to avoid blocking your main application flow. Process results in the background.
await auditAsync(text)Selective Auditing
Only audit critical responses. For low-stakes queries, you may skip auditing to reduce costs and latency.
Regional Deployment
Deploy AletheionGuard in the same region as your application to minimize network latency. Enterprise plans support multi-region deployments.