LLM Evaluation, Safety & Governance
Certificate of Completion by Code.Hub
This course provides a structured, engineering-focused approach to evaluating, securing, and governing Large Language Model (LLM) systems in production environments. It covers methodologies for assessing model quality, reliability, and alignment with business requirements. Participants will learn how to design evaluation pipelines, measure performance using quantitative and qualitative metrics, and detect issues such as hallucinations and bias. The course also explores safety mechanisms including prompt injection defense, output validation, and content filtering. Governance topics include compliance, auditability, and responsible AI practices in enterprise systems. Hands-on labs focus on implementing evaluation frameworks and safety guardrails using real-world scenarios. By the end of the course, participants will be able to deploy trustworthy, monitored, and compliant LLM systems.
By the end of this module, participants will be able to:
- Design and implement evaluation pipelines for LLM-based systems
- Measure model performance using accuracy, relevance, and robustness metrics
- Apply safety mechanisms to mitigate prompt injection, bias, and harmful outputs
- Implement governance frameworks for compliance and responsible AI usage
- Monitor, audit, and continuously improve LLM systems in production
Foundations of LLM Evaluation & Safety LLM Risks & Failure Modes
Introduction to LLM Evaluation
- Why evaluation is critical
- Types of evaluation (offline, online)
- Common LLM failure modes
AI Practice: Use AI to analyze incorrect outputs and classify failure types
Metrics & Evaluation Frameworks
- Accuracy, relevance, precision
- Human vs automated evaluation
- Benchmarking strategies
AI Practice: Design evaluation criteria using AI for a given use case
Evaluation Techniques Testing Strategies
Prompt & Output Testing
- Test case generation
- Prompt sensitivity testing
- Regression testing
AI Practice: Generate test cases and prompts to validate system behavior
RAG Evaluation
- Evaluating retrieval quality
- Context relevance
- Hallucination detection
AI Practice: Evaluate RAG outputs using AI-based scoring
Safety Mechanisms Secure AI Systems
Prompt Injection & Threats
- Injection attacks
- Jailbreaking techniques
- Threat modeling
AI Practice: Simulate prompt injection and test defenses
Guardrails & Validation
- Input/output filtering
- Policy enforcement
- Safe execution layers
AI Practice: Implement guardrails using AI-generated rules
Governance & Compliance Responsible AI
Governance Frameworks
- Responsible AI principles
- Compliance requirements
- Risk management
AI Practice: Design governance policy using AI guidance
Monitoring, Auditing & Capstone
- Logging and auditing
- Continuous evaluation
- End-to-end system validation
AI Practice: Build evaluation + safety pipeline for a real system
Roles:
- AI Engineer
- Data Engineer
- Machine Learning Engineer
- AI Solutions Architect
Seniority:
- Mid-Level, Senior
- Basic understanding of LLMs, prompt engineering, and API-based AI systems
- Experience with programming (Python or .NET) and backend/API development
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
Interested for
