LLM Evaluation, Safety & Governance
Description
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.
🕒 Duration: 16 hours
👥 Target Audience:
- Roles: AI Engineer, Data Engineer, Machine Learning Engineer, AI Solutions Architect
- Seniority: Mid-Level, Senior
Webinar Content
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Module 1: Foundations of LLM Evaluation & Safety
LLM Risks & Failure Modes |
Introduction to LLM Evaluation |
AI Practice: Use AI to analyze incorrect outputs and classify failure types |
| Metrics & Evaluation Frameworks |
AI Practice: Design evaluation criteria using AI for a given use case |
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Module 2: Evaluation Techniques
Testing Strategies |
Prompt & Output Testing |
AI Practice: Generate test cases and prompts to validate system behavior |
| RAG Evaluation |
AI Practice: Evaluate RAG outputs using AI-based scoring |
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Module 3: Safety Mechanisms
Secure AI Systems |
Prompt Injection & Threats |
AI Practice: Simulate prompt injection and test defenses |
| Guardrails & Validation |
AI Practice: Implement guardrails using AI-generated rules |
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Module 4: Governance & Compliance
Responsible AI |
Governance Frameworks |
AI Practice: Design governance policy using AI guidance |
| Monitoring, Auditing & Capstone |
AI Practice: Build evaluation + safety pipeline for a real system |
Learning Objectives:
After attending this webinar 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
Prerequisite Knowledge
- Basic understanding of LLMs, prompt engineering, and API-based AI systems
- Experience with programming (Python or .NET) and backend/API development
