🤖 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

AI FearFilter Academy

Engineering CurriculumAI FearFilter Academy

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