Develop AI Agents with OpenAI Agent Development Kit
About This Course
This WSQ Develop AI Agents with OpenAI Agent Development Kit course equips learners with practical skills to build, evaluate, and fine-tune AI agents using OpenAI's latest tools. Participants will gain deep insights into large language models (LLMs), understand their applications, and learn to work with word embeddings and vector databases to implement retrieval-augmented generation (RAG). The course also covers assessing model strengths and limitations and explores the integration of AI agents into real-world product development workflows using Langchain and function calling.
Learners will be introduced to OpenAI’s Agent Development Kit (ADK) to create intelligent agents, build RAG pipelines, orchestrate multi-agent workflows, and use Model Context Protocol (MCP) for custom tool creation. The course concludes with techniques for fine-tuning and evaluating LLMs to improve performance and task-specific accuracy. This course is ideal for tech professionals and developers looking to enhance their capabilities in AI solution development.
What You'll Learn
LO1: Analyze AI applications using large language models (LLM)
LO2: Establish LLM algorithm methodologies with word embedding and vector databases
LO3: Identify and evaluate strengths, limitations of LLM applications, and effectiveness in various tasks.
LO4: Assess LLM application feasibility in product development using function calling and langchain.
LO5: Evaluate and finetune LLM models for application improvement
Course Outline:
Topic 1 Introduction to OpenAI Agent Development Kit
What are AI Agents
Applications and use cases of AI Agents
Create an AI Agent with OpenAI Agent Development Kit (ADK)
Topic 2 Building RAG Agent
Introduction to Retrival Augmented Generation (RAG)
Word Embedding and Vector Databases
Create a RAG Agent
Topic 3 Orchestrating Multiple Agents
Introduction to Multiple Agents Orchestration
Sequential and Parallel Workflow
Handoff and Routing
Topic 4 Model context protocol (MCP)
Creating Custom Tools
Agent as Tools
Introduction to Model context protocol (MCP)
Topic 5 Fine Tuning LLM Models
Why fine tuning LLM models
Training and evaluation of fine-tuned LLM models
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target Age Group: 21 to 65 years old