WSQ - Build LLM Applications Using Flowise and LangChain

Topic 1 Overview of Large Language Model (LLM)

• What is Large Language Model?
• Opportunities LLM applications
• Use cases of LLM applications

Topic 2 Building LLM Applications with Flowise

• Flowise and Features
• Assistance and LLM
• Build Single Agent Workflow with Chatflow
• Build Multi Agent Workforce wit hAgentflow

Topic 3 LLM Application Development with LangChain

• Buiild LLM Application with Chains
• Build Agentic Workflows with Langchain

Topic 4 Build RAG Application with LangChain

• Overview of Retrieval Augmented Generation (RAG)
• Buiild RAG Applications with Langchain

Final Assessment

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

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions
• 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: 21-65 years old.

Software:

You can download and install the following software:

• OpenAI API
• Langchain

Hardware: Windows and Mac Laptops

Hardware: Laptop

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

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

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

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

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. 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.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

• NLP (Natural Language Processing) Engineer
• Machine Learning Engineer
• AI Application Developer
• Chatbot Developer
• AI Research Scientist
• AI Product Manager
• Data Scientist specializing in text data
• Conversational AI Designer
• AI Integration Specialist
• Language Model Trainer
• Content Personalization Engineer
• AI Platform Engineer
• Cognitive Computing Specialist
• AI Technical Consultant
• AI Software Architect

Tan Woei Ming: Tan Woei Ming is a data scientist and AI engineer with more than 15 years of experience in machine learning, deep learning, and intelligent automation. With a Master’s in Intelligent Systems from the National University of Singapore and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led multiple AI projects in predictive analytics and image recognition at Micron Semiconductor Asia. His expertise lies in developing data-driven solutions that integrate large language models (LLMs), neural networks, and computer vision technologies.

In “Build LLM Applications Using Flowise and LangChain,” Woei Ming helps learners understand how to design, implement, and deploy intelligent systems that leverage LLMs for real-world problem-solving. His sessions cover building modular AI pipelines, integrating Flowise for workflow orchestration, and using LangChain for context-aware reasoning. With his strong technical foundation, he enables participants to bridge data science with generative AI development for industrial and enterprise applications.

Yeo Hwee Theng: Yeo Hwee Theng is a data and AI strategist with extensive experience in enterprise analytics, data product design, and applied AI solutions. As the Data & Analytics Product Lead at Amplify Health, she drives AI adoption and data transformation initiatives across the healthcare sector. Her previous roles include AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, where she led projects in large-scale machine learning deployment. She holds a Master of Technology in Enterprise Business Analytics from NUS and an Advanced Certificate in Learning and Performance (ACLP).

In “Build LLM Applications Using Flowise and LangChain,” Hwee Theng teaches professionals how to harness the power of LLMs for data-driven innovation and automation. Her sessions focus on structuring conversational AI, managing prompt engineering workflows, and integrating LangChain with business data systems. By combining strategic data design with practical implementation, she equips learners to build enterprise-grade AI applications that enhance decision-making and operational intelligence.

Teh Siew Yee: Teh Siew Yee is a data analytics and digital transformation leader with over 20 years of experience in IT, banking, and manufacturing sectors. He has held senior leadership roles at organizations including Hewlett-Packard, Standard Chartered, and TikTok, leading teams in AI governance, analytics, and business transformation. He holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Engineering from NTU.

In “Build LLM Applications Using Flowise and LangChain,” Siew Yee focuses on helping participants understand how to design scalable AI systems that combine LLM reasoning with data analytics workflows. His sessions explore the integration of LangChain with enterprise databases and Flowise automation pipelines. By linking technical design with strategic business outcomes, he empowers learners to deploy AI solutions that improve productivity, insight generation, and customer engagement.

Truman Ng: Truman Ng is a senior cloud and AI infrastructure specialist with more than two decades of experience in enterprise IT, cybersecurity, and automation. A PMP, ACTA, and Huawei HCIE-certified professional, he has trained global teams in cloud computing, AI system integration, and DevOps workflows. His expertise lies in architecting hybrid AI infrastructures that combine scalability, performance, and security for real-world enterprise deployment.

In “Build LLM Applications Using Flowise and LangChain,” Truman provides technical guidance on integrating LLM-based AI systems within secure cloud and enterprise environments. His sessions emphasize automation pipelines, data orchestration, and performance optimization for AI workflows. By merging infrastructure knowledge with AI engineering, he helps learners build robust, scalable, and production-ready applications using Flowise and LangChain.

James Lee Kin Nam: James Lee is a veteran digital media and IT educator with over two decades of experience in multimedia design, creative technology, and digital transformation. An Adobe Certified Expert and ACLP-qualified instructor, he has helped professionals and organizations adopt AI and automation tools to enhance productivity and innovation. His teaching approach blends creativity with technical application, making complex technologies accessible to a wide range of learners.

In “Build LLM Applications Using Flowise and LangChain,” James introduces learners to the creative and practical applications of LLMs in content generation, workflow automation, and AI-powered communication. His sessions highlight visualizing data interactions, designing user-friendly interfaces for AI tools, and integrating LangChain into creative workflows. By combining design thinking with AI tool mastery, he helps participants unlock the potential of generative AI in building intelligent, user-centric applications.