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WSQ - Develop AI Agents with OpenAI Agent Development Kit

WSQ - Develop AI Agents with OpenAI Agent Development Kit

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.

Learning Outcomes

By end of the course, learners should be able to:

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 Brochure

Download WSQ - AI Application Development with Large Language Models (LLM) Brochure

Skills Framework

This course follows the guideline of Artificial Intelligence Application in Product Development ICT-TEM-4034-1.1 under ICT Skills Framework

Certification

Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.
OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ Funding

WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Baseline	MCES / SME
$800	$72.00	$472.00	$312.00

Baseline: Singaporean/PR age 21 and above
MCES(Mid-Career Enhanced Subsidy): S'porean age 40 & above

Upon registration, we will advise further on how to tap on the WSQ Training Subsidy.

You can pay the nett fee (after the WSQ training subsidy) by the following :

SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used. Click here for SkillsFuture Credit submission

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year. Click here to submit UTAP

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form and email to us.

Course Code: TGS-2024042309

Post-Course Support

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Course Cancellation/Reschedule Policy

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom

Course Details

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

Final Assessment

Written Assessment - Short Answer Questions (WA-SAQ)
Practical Performance (PP)

Course Info

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

Able to operate using 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

Minimum Software/Hardware Requirement

Software:

Download and Install the following software

Hardware: Windows or Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity.

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Individual Sponsored Trainee

Employer Sponsored Trainee

Singapore Citizens or Singapore Permanent Residents
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.

Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.

SkillsFuture Credit:

Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

PSEA:

To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
Scroll down to "Keyword Tags" to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.
And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
Not all courses are eligible for PSEA funding.

Absentee Payroll (AP) Funding:

$4.50 per hour, capped at $100,000 per enterprise per calendar year.
AP funding will be computed based on the actual number of training hours attended by the trainee.

SFEC:

If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SSG would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
SkillsFuture Enterprise Credit (SFEC) Microsite

Steps to Apply Skills Future Claim

The staff will send you an invoice with the fee breakdown.
Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

Note

SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
UTAP support amount will be paid to training provider first and claimed after end of class by learner.

Appeal Process

The candidate has the right to disagree with the assessment decision made by the assessor.
When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
The candidate must lodge the appeal within 7 days, giving reasons for appeal
The assessor can help the candidate with writing and lodging the appeal.
he assessment manager will collect information from the candidate & assessor and give a final decision.
A record of the appeal and any subsequent actions and findings will be made.
An Assessment Appeal Panel will be formed to review and give a decision.
The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
Please click the link below to fill up the Candidates Appeal Form.

Job Roles

Data Scientist
Machine Learning Engineer
AI Specialist
Research Scientist
Software Engineer
Information Technology Consultant
Data Analyst
Business Analyst
Systems Analyst
Project Manager
Product Manager
Technical Lead
AI Architect
Data Engineering Manager
Chief Technology Officer

Trainers

Tan Woei Ming: Tan Woei Ming is a data scientist and AI engineer with over 15 years of experience specializing in machine learning, deep learning, and data automation for industrial applications. He holds a Master’s in Intelligent Systems from the National University of Singapore and a First-Class Honours in Electrical and Electronic Engineering from Nanyang Technological University. His expertise includes predictive analytics, neural network modeling, and real-time data processing for automation and robotics. Having led multiple AI deployment projects in the semiconductor industry, he brings extensive hands-on experience in integrating data science with engineering solutions.

In “AI Application Development with Large Language Models (LLM),” Woei Ming guides learners through the practical development of LLM-powered applications using frameworks such as LangChain, Hugging Face, and OpenAI APIs. His sessions emphasize model fine-tuning, inference optimization, and multimodal AI integration. By combining deep technical knowledge with industry insights, he equips participants to build and deploy intelligent, scalable AI systems that harness the power of LLMs for automation and decision intelligence.

Yeo Hwee Theng: Yeo Hwee Theng is a data science leader and AI strategist with extensive experience in driving enterprise AI adoption and analytics transformation across healthcare, fintech, and government sectors. As the Data & Analytics Product Lead at Amplify Health, she leads large-scale AI implementation projects and data platform architecture design. Previously, she served as an AI & Data Architect at Huawei International and a Senior Data Scientist at DataRobot. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).

In “AI Application Development with Large Language Models (LLM),” Hwee Theng demonstrates how to architect and operationalize AI solutions using advanced LLM frameworks. Her sessions cover prompt engineering, data pipeline design, and enterprise-scale deployment strategies. Through a balance of theory and hands-on projects, she enables learners to develop AI applications that enhance knowledge retrieval, automate decision-making, and drive intelligent business outcomes.

Teh Siew Yee: Teh Siew Yee is a seasoned data analytics and digital transformation professional with over 20 years of experience in technology, banking, and manufacturing sectors. He has held leadership roles in organizations such as Standard Chartered, Hewlett-Packard, TikTok, and SIA Engineering, leading teams in AI governance, data management, and advanced analytics. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and is an ACLP-certified trainer recognized for his practical, business-focused teaching approach.

In “AI Application Development with Large Language Models (LLM),” Siew Yee teaches participants how to design, train, and integrate LLMs for intelligent automation and data-driven applications. His sessions focus on real-world implementation—covering model orchestration, knowledge retrieval, and AI ethics in deployment. By combining business strategy with AI technology, he prepares learners to apply LLMs for digital innovation, process optimization, and intelligent customer solutions.

Truman Ng: Truman Ng is a senior IT consultant and AI systems architect with more than 20 years of experience in cloud infrastructure, cybersecurity, and intelligent automation. A PMP, ACTA, and Huawei HCIE-certified professional, he has trained global organizations in DevOps, cloud computing, and AI deployment. His expertise lies in building secure, high-performance infrastructure to support scalable AI systems and model integration.

In “AI Application Development with Large Language Models (LLM),” Truman focuses on the technical foundation of deploying and managing LLM-powered systems in enterprise and cloud environments. His sessions cover API integration, performance tuning, and infrastructure optimization. By bridging AI theory with engineering practice, he helps learners design production-ready AI workflows that balance performance, security, and scalability.

James Lee Kin Nam: James Lee is a digital media and creative technology educator with more than 20 years of experience in multimedia, automation, and applied computing. An Adobe Certified Expert and ACLP-qualified trainer, he has taught professionals in AI-assisted productivity, creative design, and digital transformation. His teaching approach focuses on making advanced technology intuitive and accessible, blending creativity with technical proficiency.

In “AI Application Development with Large Language Models (LLM),” James teaches participants to apply LLMs for content generation, automation, and creative AI application design. His sessions emphasize prompt engineering, user interaction design, and the integration of AI into productivity workflows. By combining creativity with functional AI development, he enables learners to build innovative, user-centric LLM applications that enhance engagement and efficiency.