Frequently Asked Questions: AI Workflows & Integration
How are hallucinations and faulty outputs prevented?
We implement multi-layered guardrails: Content filters for unwanted content, PII detection to protect personal data, output validation against defined schemas, and automated evaluations with Ragas against ground-truth datasets. Critical flows additionally receive human-in-the-loop checkpoints.
How are AI costs controlled?
Through multiple measures: Token budgets per user/department, intelligent provider routing (cheaper models for simple tasks), response caching for recurring queries, adaptive truncation for long inputs, and batch inference for non-time-critical tasks. You receive cost dashboards with alerting on budget overruns.
How is GDPR compliance ensured?
All flows go through PII detection before the API call. Sensitive data is masked or processed in local models. We use GDPR-compliant providers with audit logs for all LLM calls and create documentation for your data protection impact assessment.
How is AI output quality measured?
With systematic evaluations: We create ground-truth datasets for your use cases, run automated evals with Ragas/LangSmith, and track metrics like faithfulness, answer relevancy, and context precision. For RAG systems, we additionally measure retrieval quality.
How do updates and prompt changes work in production?
Prompts are versioned and deployed via CI/CD. Changes go through automated evals against baseline datasets. New versions are rolled out via canary deployment and automatically rolled back on quality issues. Feature flags enable A/B tests of different prompt variants.