Gen AI - Bespoke Chatbots
About This Course
This 2-day intermediate-level course equips participants with the essential competencies to build, configure, and deploy generative AI (GenAI) applications in real-world environments. Learners will gain hands-on experience with low-code/no-code tools, foundational model integration, Retrieval Augmented Generation (RAG) techniques, and prompt engineering. Through a blend of guided practice and troubleshooting scenarios, participants will also learn how to address common performance issues, maintain system compatibility, and appropriately escalate problems related to GenAI tools.
Designed to meet the rising industry demand for GenAI fluency, this course addresses critical skills needed across sectors such as finance, tech, healthcare, and logistics. As companies accelerate their digital transformation and integrate AI into customer service, product development, and operations, this course enables learners to contribute immediately by supporting the deployment and maintenance of AI applications aligned with business goals.
Upon completion, participants will be able to support AI implementation teams, troubleshoot AI tool performance, and develop functional prototypes using modern AI frameworks. These competencies can enhance career prospects in roles such as AI product assistants, solution developers, digital transformation specialists, or technical support staff—providing pathways for job upgrading and mobility within a growing AI-enabled economy.
What You'll Learn
• LO1: Apply systematic troubleshooting procedures to diagnose and resolve performance issues in AI applications using standard documentation and help resources
• LO2: Implement proper software configurations and system compatibility checks to ensure optimal AI application performance across different environment
• LO3: Apply knowledge of AI model limitations and error patterns to appropriately categorize, document, and escalate complex issues encountered with AI tools
• LO4: Implement Retrieval Augmented Generation (RAG) systems using vector databases and retrieval algorithms to enhance AI application responses with relevant contextual information
• LO5: Apply prompt engineering techniques and model configuration strategies to integrate foundational models into applications using low-code/no-code development platforms and specialized AI frameworks
Entry Requirements
- 1 year of working experience in any sector; and
- Workplace Literacy and Numeracy (WPLN) 4 or equivalent
*Learners without the right WPLN or equivalent qualifications can be accepted if they have at least 3 years of relevant working experience, subject to the MR’s approval.