Cracking AI & ML Evaluation System Design Interviews

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↳ What is AI Evaluation: Learn how to measure the quality of an AI system.
↳ Metrics vs Objectives: See how business goals differ from evaluation metrics.
↳ Reliability & Validity: Make sure your evaluation results are consistent and meaningful.
↳ Statistical Testing: Determine whether improvements are statistically significant.
↳ Experimental Design: Learn how to set up reliable evaluation experiments.
↳ Golden Datasets: Create strong benchmark datasets for testing.

↳ Accuracy: Measure overall prediction correctness.
↳ Precision: Measure how many positive predictions are correct.
↳ Recall: Measure how many actual positives are found.
↳ F1 Score: Equilibrate precision and recall.
↳ ROC-AUC: Evaluate model discrimination across thresholds.
↳ PR-AUC: Evaluate performance on imbalanced datasets.

↳ MAE: Find the average amount the model’s predictions are off.
↳ RMSE: Give more weight to bigger prediction mistakes.
↳ R² Score: Measure how well the model explains variance.

↳ Recall@K: Check how many important items are found in the top K results.
↳ Precision@K: Measure how accurate the top K-ranked items are.
↳ MRR: Check where the first correct answer appears in the list.
↳ MAP: Measure how good the overall ranking is.
↳ NDCG: Check ranked results by how relevant each item is.

↳ Error Analysis: Find frequent mistakes the model makes.
↳ Slice Evaluation: Check how the model performs on different parts of the data.
↳ Robustness Testing: Check if the model stays reliable with messy or tricky inputs.

↳ Human Evaluation: Have people rate the model’s answers.
↳ LLM-as-a-Judge: Use a language model to automatically check answers.
↳ Pairwise Comparison: Look at two model answers side by side to compare.
↳ Rubric Engineering: Create clear and consistent rules for evaluation.
↳ Hallucination Detection: Identify unsupported or fabricated information.
↳ Faithfulness: Check that answers agree with the given information.
↳ Groundedness: Make sure answers are backed by facts.
↳ Instruction Following: Check how well the model follows user directions.
↳ Structured Output Validation: Check whether outputs follow defined formats, such as JSON or XML.
↳ Benchmark Evaluation: Test models using common standard tests.

↳ Retrieval Quality: Measure the relevance of retrieved documents.
↳ Chunk Quality: Evaluate document chunking strategies.
↳ Embedding Quality: Assess the quality of the semantic representation.
↳ Reranker Evaluation: Measure improvements from reranking.
↳ Context Precision: Measure the relevance of the retrieved context.
↳ Context Recall: Measure completeness of retrieved context.
↳ Citation Accuracy: Verify generated citations are correct.
↳ Hallucination Analysis: Detect unsupported generated content.
↳ RAGAS: Learn automated metrics for RAG systems.
↳ DeepEval: Build automated evaluation pipelines.
↳ TruLens: Monitor and evaluate production RAG applications.

↳ Task Success: Measure whether agents complete assigned tasks.
↳ Planning Quality: Evaluate reasoning and planning ability.
↳ Tool Use: Assess effective use of external tools.
↳ Memory Evaluation: Measure long-term memory performance.
↳ Multi-turn Evaluation: Evaluate conversations across multiple interactions.
↳ Function Calling: Validate API and tool execution accuracy.
↳ Multi-agent Evaluation: Measure collaboration between agents.
↳ Agent Benchmarks: Evaluate using SWE-bench, WebArena, and GAIA.

↳ Toxicity: Detect harmful or offensive outputs.
↳ Bias: Measure fairness across users and groups.
↳ Prompt Injection: Test resistance to prompt manipulation.
↳ Jailbreak Testing: Evaluate security against adversarial prompts.
↳ Red Teaming: Discover vulnerabilities through systematic attacks.
↳ Privacy Evaluation: Detect sensitive information leakage.
↳ Governance Standards: Apply frameworks like NIST AI RMF and OWASP LLM Top 10.

↳ OpenAI Evals: Build benchmark-driven evaluation suites.
↳ DeepEval: Automate testing for LLM applications.
↳ Promptfoo: Compare prompts and model outputs.
↳ LangSmith: Trace, debug, and evaluate LLM workflows.
↳ Arize Phoenix: Monitor production AI performance.
↳ MLflow Evaluation: Track evaluation experiments and metrics.
↳ Hugging Face Evaluate: Compute standard evaluation metrics.
↳ lm-evaluation-harness: Benchmark foundation models consistently.

↳ Offline Evaluation: Test models before deployment.
↳ Online Evaluation: Measure live production performance.
↳ A/B Testing: Compare multiple model versions.
↳ Shadow Deployment: Test new models without impacting users.
↳ Drift Detection: Detect changes in data or model behavior.
↳ Latency & Cost: Optimize response speed and inference cost.
↳ Monitoring & Alerting: Continuously track production quality.

↳ Evaluation Pipelines: Automate end-to-end evaluation workflows.
↳ Dataset Versioning: Track changes to benchmark datasets.
↳ Experiment Tracking: Record evaluation runs and results.
↳ Batch Evaluation: Evaluate large datasets quickly.
↳ Distributed Evaluation: Scale assessments across multiple machines.
↳ Dashboards: Visualize evaluation measures and trends.
↳ Leaderboards: Compare models applying standardized benchmarks.

↳ ML Evaluation Library: Implement common ML evaluation measures.
↳ LLM Judge System: Build an automated LLM evaluator.
↳ RAG Evaluation Pipeline: Evaluate retrieval and generation together.
↳ AI Agent Evaluator: Measure end-to-end agent performance.
↳ Safety Benchmark Suite: Build security and alignment tests.
↳ Continuous Assessment CI/CD: Automate evaluation during deployment.
↳ Production Evaluation Dashboard: Monitor AI quality in real time.

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