Launch Pillar 04
AI & LLM Behavioral Testing & Evaluation
Evaluate LLM responses, detect hallucinations, verify prompt injection resilience, and enforce schema contracts on AI-generated outputs.
https://app.taiplabs.com/runs/ai-eval/rag-pipeline
AI Evaluation Run #120
Model: gpt-4o / claude-3-5-sonnet • RAG PipelineScore: 96.8% Accuracy
Hallucination Detection: Factual grounding verified against vector context
Grounding 99.2%Prompt Injection: Resisted system prompt override and secret exfiltration
10/10 Jailbreaks BlockedStructured Schema: Output complies with specified JSON Schema model
100% ParseableAvg Latency: 640ms • Tokens: 1,420 / runALL AI BENCHMARKS PASSED
Hallucination Detection
Verify that responses generated by your LLMs are factually grounded in retrieved knowledge rather than fabricated or hallucinated statements.
Prompt Injection Resilience
Simulate adversarial prompt attacks and jailbreaks to confirm that internal system instructions, tool credentials, and sensitive tenant prompts cannot be extracted.
Structured Output Validation
Ensure agent function calls and LLM JSON outputs adhere strictly to TypeScript interfaces and database schemas without runtime parsing errors.