Description

This comprehensive two-day course takes participants beyond basic prompting to master advanced techniques for enterprise AI deployment. Students will learn to create robust, reusable prompt templates with built-in guardrails, design retrieval-aware systems that ground AI responses in enterprise content, and implement structured outputs for downstream automation.The course emphasizes practical evaluation methodologies and operational considerations forscaling AI solutions across organizations.

Participants will gain hands-on experience designing prompt patterns that scale, implementing enterprise grounding with retrieval-augmented generation (RAG), creating structured outputs for automation, establishing quality frameworks with systematic evaluation, and deploying operational prompt systems with versioning and monitoring. Through five progressive hands-on labs, students will build a complete prompt operations toolkit ready for production deployment.

Objectives

Upon completion of this course, students will be able to:

• Design advanced prompt patterns using role/task/style frameworks, few-shot learning, and constrained outputs
• Implement enterprise grounding by designing RAG-optimized prompts with citation, chunking strategies, and context window optimization
• Build automation-ready systems using JSON schema design, tool-calling patterns, and structured output validation
• Establish quality frameworks through evaluation rubric creation, golden dataset construction ,and A/B testing methodologies
• Deploy operational prompt systems with versioning, telemetry, monitoring, and comprehensive enablement materials

Topics

• Structured prompt architecture and compound prompt design patterns
• Safe reasoning techniques and guardrail implementation
• RAG (Retrieval-Augmented Generation) prompt design and optimization
• Context window management and chunking methodologies
• JSON schema design for structured AI outputs
• Functions and tool-calling integration patterns
• Evaluation framework design and golden dataset construction
• Batch evaluation processes and A/B testing methodologies
• Prompt versioning and lifecycle management
• Telemetry, monitoring, and performance tracking for production systems

Audience

This course is designed for power users of AI platforms, product and operations leads, solution owners and architects, business analysts working with AI tools, and technical leads implementingAI solutions. Students should have experience working in enterprise environments with an understanding of organizational policies, procedures, and governance requirements.

Prerequisites

Students should have:

• Basic GenAI familiarity and experience with AI platforms (Microsoft 365 Copilot, AmazonBedrock, Azure OpenAI, or similar)
• Understanding of enterprise content management concepts and access patterns
• Familiarity with JSON structure and basic API concepts (helpful but not required)
• Experience working in organizational settings with policies and governance
• Modern web browser with internet access
• Access to at least one enterprise AI platform (Microsoft 365 Copilot, Amazon Bedrock, AzureOpenAI Studio, or similar)

Duration

2 Days