Build Agentic AI and NLP Applications with Langflow
About This Course
This WSQ Build Agentic AI and NLP Applications with Langflow equips learners with essential skills in Natural Language Processing (NLP) and agentic AI development. Participants will gain hands-on experience in performing text representation using word embeddings, language modeling, and machine learning-based text classification. The course also covers strategic methods to enhance memory networks in AI agents, providing a strong foundation in building intelligent, language-based systems.
Through step-by-step guidance on using Langflow, learners will explore how to build and deploy LLM-powered chatbots, Retrieval-Augmented Generation (RAG) systems, and multi-agent workflows. The course also introduces Streamlit for developing custom AI interfaces and MCP for scalable agent communication. This training is ideal for professionals looking to deepen their technical skills and apply AI in real-world use cases.
What You'll Learn
- identify the tasks associated with natural language processing (NLP)
- perform text representation using word embedding
- perform language processing and modeling
- build text classification using machine learning
- determine strategies to enhance memory networks
Course Outline:
Topic 1 Introduction to Agentic AI and Langflow
Overview of Agentic AI and LLM
Use Cases of Agentic AI and LLM
Build and Deploy Your Non Code LLM Powered Chatbot
Installation of Langflow
Explore Langflow Interface
Build and Deploy a Simple Agentic AI Flow with Langflow
Topic 2 Build a RAG with Langflow
Overview of Tokenization, Embedding and Vector Store
Introduction to Retrieval Augmented Generation (RAG)
Chucking Strategies
Best Practices of Using RAG
Build a RAG workflow with Langflow
Topic 3 Build an AI Agent with Langflow
Overview of AI agent fundamentals - tools, memories and LLM
Reasoning framework for AI agents
Build a sequential task multi agent workflow on Langflow
Build a travel planning agent workflow on Langflow
Topic 4 Build a Streamlit AI Chabot App
Streamlit fundamentals
Create a LLM chatbot with Streamlit
Create and deploy a text classifier with Streamlit
Topic 5 Model Context Protocol (MCP)
Overview of Model Context Protocol MCP
Create MCP Client and Server with Langflow
Deploy a Langflow MCP server
Entry Requirements
Knowledge and Skills
• Minimum 3 GCE 'O' Level passes including English, or Workplace Literacy (WPL) Level 5 (average of Reading, Listening, Speaking and Writing scores)
• Able to operate basic computer functions including web browsing, file management, and use of common office applications
• Basic understanding of business operations and organisational processes
Attitude
• Positive learning attitude with willingness to embrace new technologies and innovative approaches
• Openness to exploring AI-driven tools and methods for business problem-solving
• Enthusiastic and self-motivated learner with a collaborative mindset
Experience
• Minimum of 2 years of working experience in any industry
• Prior experience in business operations, project coordination, or process improvement is advantageous but not mandatory
• No prior programming or coding experience is required
Target age group: 21–65 years old