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🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile

Hands-on Mastery in LLM Evaluation & AI Testing

Master systematic LLM evaluation, quality benchmarking, automated AI testing, hallucination detection, RAG triads, agent validation, red teaming, and CI/CD quality gates with your dedicated AI Agent coach.

40 Modules • 9 Production Projects • Dedicated AI Coach
40 Modules
AI FearFilter Faculty

What You Will Learn

Design and curate representative golden test sets with multi-annotator consensus ground truth.
Quantify exact output accuracy, semantic similarity, and hallucination rates with statistical rigor.
Evaluate production RAG pipelines using Context Relevance, Groundedness, and Answer Relevance metrics.
Benchmark autonomous AI agents for tool selection correctness, trajectory consistency, and recovery.
Build reliable, de-biased LLM-as-a-Judge pipelines with calibrated rubrics and position swaps.
Automate regression testing pipelines inside GitHub Actions CI/CD with explicit quality thresholds.
Monitor live production AI traffic for prompt drift, latency bottlenecks, and safety violations.

Curriculum & Weekly Roadmap

40 Structured Modules

Module 1: Introduction to LLM Evaluation and AI Testing

  • What is LLM Evaluation?
  • The Need for Systematic AI Testing
  • Core Dimensions of AI Quality

Module 2: Why AI Applications Need Testing

  • Failure Modes of LLMs in Production
  • Real-World Consequences of Untested AI
  • Building a Safety Net for Stochastic Models

Module 3: AI Testing vs Traditional Software Testing

  • Deterministic Code vs Probabilistic Outputs
  • Unit Testing vs Semantic Quality Verification
  • The Hybrid Testing Paradigm

Module 4: What Makes a Good AI Output?

  • Defining Output Desirability
  • Accuracy vs Fluency vs Conciseness
  • Aligning Output with User Intent

Module 5: Defining Quality Criteria

  • Translating Product Requirements into AI Metrics
  • Establishing Objective Scoring Criteria
  • Quantifying Subjective Qualities

Module 6: Evaluation Datasets and Test Cases

  • Anatomy of an AI Test Case
  • Gathering Representative Input Data
  • Structuring Test Suites for Scale

Module 7: Golden Test Sets

  • What is a Golden Dataset?
  • Curating and Labeling Ground Truth
  • Maintaining and Versioning Golden Sets

Module 8: Expected Output vs Acceptable Output

  • Exact Matches vs Semantic Equivalence
  • Defining Output Tolerance Bands
  • Handling Variational Phrasing

Module 9: Deterministic vs Non-Deterministic AI Testing

  • Understanding Temperature and Seed Control
  • Statistical Repeatability and Sampling
  • Managing Flakiness in AI Test Suites

Module 10: LLM Evaluation Fundamentals

  • Component-Level vs End-to-End Evaluation
  • Heuristic, Model-Based, and Human Metrics
  • The Evaluation Lifecycle

Module 11: Accuracy, Relevance and Completeness

  • Measuring Factual Accuracy
  • Quantifying Information Relevance
  • Assessing Response Completeness and Coverage

Module 12: Groundedness and Factuality

  • Grounded vs Ungrounded Claims
  • Measuring Factuality Against Source References
  • Automated Fact Verification Pipelines

Module 13: Faithfulness and Hallucination Testing

  • Types of Hallucinations: Intrinsic vs Extrinsic
  • Faithfulness Metric Calculation
  • Stress Testing for Hallucinations

Module 14: Context Relevance

  • What is Context Relevance?
  • Measuring Signal-to-Noise in Retrieved Context
  • Impact of Irrelevant Context on LLM Reasoning

Module 15: Instruction Following

  • Evaluating Constraint Adherence
  • Negative Constraint Testing (What NOT to do)
  • Complex Multi-Constraint Verification

Module 16: Safety and Responsible AI Testing

  • Red Teaming and Adversarial Prompt Injections
  • Jailbreak Detection and Defense Testing
  • PII and Sensitive Data Leakage Testing

Module 17: Bias and Toxicity Testing

  • Identifying Demographic and Cognitive Bias
  • Measuring Toxicity, Hate Speech, and Harm
  • Fairness Auditing across User Segments

Module 18: Prompt Testing

  • Systematic Prompt Variation Experiments
  • Sensitivity Testing (Perturbations and Edge Cases)
  • Few-Shot Example Selection and Impact Analysis

Module 19: Prompt Regression Testing

  • Detecting Unintended Side Effects of Prompt Edits
  • Automated Diff and Quality Degradation Alerts
  • Building a Prompt Regression Test Suite

Module 20: Testing RAG Applications

  • The RAG Triad Architecture
  • Isolating Retrieval Failures vs Generation Failures
  • End-to-End RAG Quality Benchmarking

Module 21: Testing Retrieval Quality

  • Precision@K and Recall@K for Vector Search
  • Mean Reciprocal Rank (MRR) and NDCG
  • Evaluating Hybrid Keyword-Vector Retrieval

Module 22: Evaluating Embeddings and Retrieval

  • Embedding Model Benchmarking (MTEB)
  • Measuring Semantic Cluster Separability
  • Chunking Strategy Impact on Retrieval Accuracy

Module 23: Testing AI Agents

  • Agent Architecture Failure Modes
  • Evaluating Reasoning Traces and Planning
  • State Tracking and Context Memory Verification

Module 24: Tool-Calling Evaluation

  • Tool Selection Accuracy
  • Argument Schema and Parameter Validation
  • Tool Call Error Recovery Testing

Module 25: Agent Task Completion Testing

  • Success Rate Metric for Autonomous Goals
  • Measuring Efficiency and Step Count
  • Edge Case and Goal Drift Testing

Module 26: Multi-Step AI Workflow Testing

  • Error Compounding in Multi-Agent Chains
  • Intermediate Node State Assertions
  • End-to-End Integration Testing for Agent Workflows

Module 27: Human Evaluation and Human-in-the-Loop

  • Designing Human Annotation Guidelines
  • Inter-Annotator Agreement (Cohen's Kappa)
  • Scaling Human Feedback Cost-Effectively

Module 28: LLM-as-a-Judge

  • How LLM-as-a-Judge Works
  • Mitigating Judge Biases
  • Pairwise Comparison vs Absolute Scoring

Module 29: Designing Reliable Evaluation Rubrics

  • Anatomy of an Unambiguous Rubric
  • Few-Shot Calibration for LLM Judges
  • Creating Multi-Tier Criteria with Concrete Anchors

Module 30: Evaluation Metrics and Scoring

  • Exact Match, BLEU, and ROUGE Limitations
  • Semantic Similarity (BERTScore, Cosine Distance)
  • Composite Quality Scoring Frameworks

Module 31: Error Analysis and Failure Classification

  • Categorizing AI Failure Taxonomies
  • Root Cause Analysis: Prompt, Model, Context, or Data
  • Prioritizing Fixes by Business Impact

Module 32: Debugging AI Application Failures

  • Logging and Tracing Execution Steps
  • Reproducing Stochastic AI Bugs
  • Surgical Prompt and Context Interventions

Module 33: Regression Testing for AI Applications

  • Establishing Quality Baselines
  • Automated Regression Detection
  • Safe Rollouts and Canary Deployments for AI

Module 34: Automated Evaluation Pipelines

  • Structuring Batch Evaluation Workflows
  • Parallelizing Model Invocations and Scorer Nodes
  • Integrating Frameworks (Ragas, TruLens, DeepEval)

Module 35: AI Testing in CI/CD

  • Integrating AI Evals into GitHub Actions
  • Setting Merge Quality Gates and PR Comments
  • Managing Evaluation Latency and API Budgets

Module 36: Production Monitoring and Evaluation

  • Online vs Offline Evaluation
  • Telemetry, User Feedback, and Implicit Signals
  • Real-Time Anomaly and Guardrail Alerting

Module 37: Evaluation Drift and Model Changes

  • Detecting Data Drift and Concept Drift in AI
  • Model Version Upgrade Regression Testing
  • Continuous Re-evaluating with Production Traffic Samples

Module 38: Cost, Latency and Performance Testing

  • Benchmarking Time-To-First-Token and Throughput
  • Token Economics and Cost Optimization Testing
  • Load Testing and Concurrency Stress Testing

Module 39: Building an AI Evaluation System

  • Architecture of an Enterprise Eval Platform
  • Test Case Management, Versioning, and Reporting
  • Closing the Feedback Loop to Improve Models

Module 40: Final Production-Style LLM Evaluation & AI Testing Project

  • Capstone Project Architecture and Scope
  • Implementation, Rubrics, and Automated CI Pipeline
  • Production Defense, Evidence Gathering, and Skill Verification

Who This Course Is For

Aspiring AI Engineers, QA Automation Engineers, Software Developers, Systems Architects, and CS Students aiming to build and deploy production-grade LLM evaluation harnesses, RAG benchmarking, and automated AI testing pipelines.

Key Skills Developed:

Foundations of AI Quality, Non-Determinism & Evaluation FramingEvaluation Criteria Definition & Metric FormulationGolden Test Case & Synthetic Dataset GenerationRule-Based, Overlap (BLEU/ROUGE), and Semantic Similarity MetricsHallucination Detection, Faithfulness & Grounding EvaluationEnd-to-End RAG Triad Evaluation (Relevance, Groundedness, Retrieval)Autonomous AI Agent, Tool Calling & Trajectory TestingAdversarial Red Teaming, Jailbreak Probing & Prompt Injection DefenseLLM-as-a-Judge Design, Calibration, Rubrics & Bias MitigationAutomated CI/CD Quality Gates & Production Telemetry Monitoring

Course Faculty & Development

AI FearFilter Faculty

AI Testing & Evaluation Engineering Team

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