Changi, Singapore

Generative AI

LangChain for Enterprise AI Applicationsin Changi

40Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Changi, Singapore. Dates, seating capacity, facilities and the final venue are confirmed before booking.

Learning in Changi, Singapore

City details

Changi is part of Singapore's connected, multicultural and innovation-focused professional environment.

City speciality

Connectivity, innovation and multicultural communities

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Explore LangChain for Enterprise AI Applications

LangChain for Enterprise AI Applications training in Changi, Singapore provides practical knowledge for building scalable AI solutions using LangChain, LLMs, RAG, agents, and enterprise data. Learn prompt engineering, document processing, retrieval, tool integration, memory, and workflow orchestration through hands-on learning. This course helps AI developers, software professionals, and technical teams build production-ready Generative AI applications and intelligent enterprise solutions using modern LangChain frameworks and best practices.

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Your learning location

Learning from Changi

Changi is part of Singapore's connected, multicultural and innovation-focused professional environment.

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City-page course benefits

Build Enterprise-Ready AI Applications – Learn how to use LangChain to develop scalable, intelligent, and production-focused AI applications for organizations in Changi, Singapore.
Master LLM Application Development – Gain practical skills in integrating Large Language Models, prompts, chains, tools, and workflows into enterprise AI solutions.
Develop Advanced RAG Solutions – Learn to connect enterprise data with LLMs using document processing, embeddings, vector databases, retrieval, and RAG techniques.
Create AI Agents and Intelligent Workflows – Understand how to build agent-based applications capable of using tools, APIs, and enterprise resources to complete complex tasks.
Integrate Enterprise Data and APIs – Develop skills for connecting LangChain applications with databases, APIs, documents, and external enterprise systems.
Improve AI Application Scalability – Learn practical approaches for designing maintainable and scalable LangChain applications suitable for enterprise environments.
Enhance Your Generative AI Career – Build job-relevant LangChain and LLM application development skills for AI and software development opportunities in Changi, Singapore.
Gain Hands-On Enterprise Experience – Apply LangChain concepts through practical exercises and real-world scenarios to strengthen your enterprise Generative AI development capabilities.

Course curriculum

Module 1: Introduction to LangChain for Enterprise AI
- LangChain architecture and core components
- Enterprise Generative AI application concepts
- LLM-powered application use cases
- LangChain ecosystem and development workflow
Module 2: LLMs and Model Integration
- Connecting OpenAI, Azure OpenAI, Gemini, and other LLM providers
- Model configuration and parameter management
- Prompt templates and structured outputs
- Managing model responses and errors
Module 3: Prompt Engineering with LangChain
- Designing effective prompts
- System, user, and dynamic prompts
- Prompt templates and reusable components
- Output parsing and validation
Module 4: LangChain Chains and Runnables
- Understanding chains and execution workflows
- LangChain Expression Language (LCEL)
- Runnable sequences and parallel execution
- Building reusable AI pipelines
Module 5: Enterprise Data Integration
- Loading documents from different sources
- Document loaders and data connectors
- Text splitting and document transformation
- Managing enterprise knowledge sources
Module 6: Embeddings and Vector Databases
- Embedding models and semantic representations
- Vector database concepts
- Storing and retrieving vectorized data
- Similarity search and metadata filtering
Module 7: Retrieval-Augmented Generation (RAG)
- Designing RAG pipelines with LangChain
- Document indexing and retrieval
- Retriever configuration and optimization
- Context-aware response generation
Module 8: Advanced Retrieval Techniques
- Hybrid search strategies
- Query transformation and rewriting
- Reranking retrieved information
- Improving retrieval relevance and response quality
Module 9: LangChain Agents and Tools
- Understanding AI agents
- Creating and integrating tools
- Tool calling and function execution
- Building agent-based enterprise workflows
Module 10: Memory and Conversation Management
- Managing conversational context
- Conversation history and state
- Designing context-aware applications
- Handling long-running interactions
Module 11: Enterprise AI Workflows
- Multi-step AI workflows
- Connecting LLMs with APIs and business systems
- Workflow orchestration
- Automating enterprise processes with AI
Module 12: LangGraph for Stateful Applications
- Introduction to LangGraph
- Nodes, edges, and state management
- Building controllable AI workflows
- Managing complex agent interactions
Module 13: Evaluation and Observability
- Evaluating LLM application responses
- Retrieval and generation quality assessment
- Tracing AI workflows
- Identifying errors and improving application performance
Module 14: Enterprise AI Security
- Secure handling of enterprise data
- Prompt injection awareness
- Access control and authentication concepts
- Data privacy considerations for AI applications
Module 15: Deployment and Production Considerations
- Preparing LangChain applications for deployment
- API-based application integration
- Performance and scalability considerations
- Monitoring production AI applications
Module 16: Capstone Enterprise AI Project
- Design an enterprise-focused LangChain application
- Integrate LLMs, RAG, tools, and enterprise data
- Test and evaluate application performance
- Present a complete AI solution for use cases in Changi, Singapore.

Exam details

Exam Level: Intermediate – Enterprise Generative AI Developer

Exam Type: Online computer-based assessment consisting of multiple-choice questions and practical application-based tasks.

Number of Questions: 40 questions

Exam Duration: 90 minutes

Passing Score: 70% (28 out of 40)

Practical Assessment: Candidates may be required to design or troubleshoot LangChain workflows, implement retrieval components, integrate tools, or develop an enterprise AI use case.

Prerequisites: Basic Python programming, LLM concepts, APIs, and familiarity with Generative AI are recommended.

Assessment Location: Learners can complete the assessment online from Changi, Singapore.

Certification: Successful candidates receive a StepMerit Learning Course Completion Certificate.

Plan your learning

Who this course is for

AI and Generative AI Developers – Professionals who want to build enterprise AI applications using LangChain, LLMs, RAG, agents, and AI workflows in Changi, Singapore . Software Developers – Developers seeking to integrate LLM capabilities, APIs, tools, and intelligent workflows into modern enterprise applications. Python Developers – Python professionals interested in developing practical LangChain applications and LLM-powered solutions. Machine Learning Engineers – ML professionals looking to expand their expertise into Generative AI, RAG, agents, and enterprise LLM applications. LLM Application Developers – Developers who want to design, integrate, and optimize applications powered by…

Before you begin

Python Programming: Basic to intermediate Python programming knowledge is recommended for hands-on LangChain development. Generative AI & LLM Fundamentals: Familiarity with Generative AI, Large Language Models (LLMs), prompts, tokens, and model APIs is helpful. API Knowledge: Basic understanding of REST APIs, JSON, and integrating external services is recommended. RAG Fundamentals: Basic knowledge of Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search is beneficial. Software Development: Experience with application development, debugging, and working with development environments will support practical learning. Cloud & Databases: Basic familiarity with…

Learning outcomes

Problem Statement – A healthcare organisation manages large volumes of patient-related information, clinical documents, medical guidelines, hospital policies, appointment information, FAQs, and healthcare knowledge resources. Employees and healthcare teams may spend significant time searching through multiple sources, summarising documents, answering routine enquiries, and preparing information for operational workflows. A standalone LLM may not have access to organisation-specific information and can produce incomplete, outdated, or inaccurate responses. The challenge is to develop an enterprise AI application using LangChain that can connect LLMs with healthcare data, external tools…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional & Training Experience: 5+ Years Course Training Experience: 150+ Hours of training focused on LangChain, enterprise AI applications, LLM integration, RAG, prompt engineering, chains, tools, agents, embeddings, vector databases, and AI workflow development. Candidates Trained: 200+ candidates, including AI professionals, software developers, Python developers, machine learning engineers, data professionals, IT professionals, and Generative AI enthusiasts. Training Delivery Experience: Experienced in instructor-led online and classroom training with practical demonstrations, coding exercises, hands-on labs, assignments, case studies, and project-based learning. LangChain…

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Insights for LangChain for Enterprise AI Applications

Learning insight 01

LangChain for Enterprise AI Applications: Complete Guide to LLMs, RAG, AI Agents, Workflows & Generative AI

Generative AI is transforming how organizations build applications, automate workflows, search enterprise information, and interact with business data. However, developing production-ready AI applications requires more than connecting an application to a Large Language Model (LLM). Developers need reliable frameworks…

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