Evaluation Driven Design Patterns
Delve into evaluation-driven design for agentic AI, featuring human-in-the-loop architecture, batch evaluation with your data, tracing-based scheduling, safety evaluators, and adversarial simulator usage. Key Skills is testing and benchmarking AI agents, integrating human feedback loops, ensuring ethical and safe AI operation.
Human-in-the-loop in agentic architecture
What is multi-Agent Collaboration?
Human-in-the-loop (HITL) in agentic architecture refers to a system design approach where human oversight, intervention, or collaboration is integrated into the AI-driven process. This ensures that AI agents operate within ethical, safe, and effective boundaries, particularly in complex or high-stakes scenarios.
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Human Oversight & Control: Ensures that AI decisions are reviewed, validated, or overridden by humans before execution. Example: AI suggests business strategies, but executives make the final call.
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Continuous Learning & Adaptation: AI improves over time by learning from human feedback, refining its decision-making process. Example: Reinforcement Learning with Human Feedback (RLHF) in AI chatbots.
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Intervention for Critical Decisions: In high-risk or complex situations, humans intervene to ensure accuracy and compliance. Example: In medical AI, doctors approve diagnoses before prescribing treatments.
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Hybrid Decision-Making: AI handles repetitive or high-speed tasks, while humans provide strategic oversight. Example: AI filters job applications, but recruiters make final hiring decisions.
Key Advantages
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Increases Reliability & Trust: Reduces AI errors and builds confidence in AI-driven processes. Ensures decisions are ethical, fair, and compliant with regulations.
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Enhances Adaptability & Learning: AI continuously evolves based on human feedback, improving accuracy and performance. Avoids rigid automation, allowing AI to adjust to new scenarios.
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Reduces Risks & Prevents Biases: Human intervention helps correct AI biases and prevent unintended consequences. Especially crucial in AI-driven hiring, medical diagnosis, and financial services.
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Optimizes Efficiency & Productivity: AI accelerates routine tasks, while humans focus on higher-level strategic decisions. Balances automation with human expertise, leading to better outcomes.
Azure Evaluation SDK
To thoroughly assess the performance of your generative AI application when applied to a substantial dataset, you can evaluate a Generative AI application in your development environment with the Azure AI evaluation SDK.
| Category | Evaluator class |
|---|---|
| Performance and quality (AI-assisted) | GroundednessEvaluator, GroundednessProEvaluator, RetrievalEvaluator, RelevanceEvaluator, CoherenceEvaluator, FluencyEvaluator, SimilarityEvaluator |
| Performance and quality (NLP) | F1ScoreEvaluator, RougeScoreEvaluator, GleuScoreEvaluator, BleuScoreEvaluator, MeteorScoreEvaluator |
| Risk and safety (AI-assisted) | ViolenceEvaluator, SexualEvaluator, SelfHarmEvaluator, HateUnfairnessEvaluator, IndirectAttackEvaluator, ProtectedMaterialEvaluator |
| Composite | QAEvaluator, ContentSafetyEvaluator |
Performance and Quality Evaluators
AI-assisted Evaluators:
GroundednessEvaluator: Assesses the accuracy of responses based on provided context. GroundednessProEvaluator: Similar to GroundednessEvaluator but uses Azure AI Content Safety. RetrievalEvaluator: Evaluates the effectiveness of retrieval-augmented generation. RelevanceEvaluator: Measures how relevant the response is to the query. CoherenceEvaluator: Checks the logical flow and consistency of the response. FluencyEvaluator: Evaluates the grammatical correctness and naturalness of the response. SimilarityEvaluator: Measures the similarity between generated responses and ground truth.
NLP Evaluators:
F1ScoreEvaluator: Calculates the F1 score for precision and recall. RougeScoreEvaluator: Measures overlap with reference texts using ROUGE metrics. GleuScoreEvaluator: Evaluates using GLEU score. BleuScoreEvaluator: Uses BLEU score for evaluating machine translations. MeteorScoreEvaluator: Measures using METEOR score.
Risk and Safety Evaluators
AI-assisted Evaluators:
ViolenceEvaluator: Detects violent content. SexualEvaluator: Identifies sexually explicit content. SelfHarmEvaluator: Detects content related to self-harm. HateUnfairnessEvaluator: Identifies hate speech and unfair content. IndirectAttackEvaluator: Evaluates vulnerability to indirect attack jailbreaks. ProtectedMaterialEvaluator: Detects protected material.
Composite Evaluators:
QAEvaluator: Combines multiple quality evaluators for comprehensive assessment. ContentSafetyEvaluator: Combines multiple safety evaluators for overall safety assessment.