Building Enterprise Chatbots using RAGin Adelaide

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Explore Building Enterprise Chatbots using RAG

Learn to design and develop intelligent enterprise chatbots with StepMerit's Building Enterprise Chatbots Using RAG course. Explore document processing, embeddings, vector databases, large language models, and Retrieval-Augmented Generation to build context-aware conversational AI solutions. Develop practical skills in enterprise knowledge retrieval, chatbot integration, response evaluation, and secure information access. Strengthen your Generative AI development capabilities through industry-focused training in Adelaide, Australia.

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Learning from Adelaide

Adelaide combines a high-quality learning environment, cultural diversity and active professional communities.

Course curriculum

Module 1: Introduction to Enterprise Chatbots and RAG
- Understand enterprise conversational AI, chatbot architectures, and business use cases.
- Explore Retrieval-Augmented Generation (RAG) and how it connects external knowledge with large language models (LLMs).
Module 2: Python and Development Environment Setup
- Set up Python environments, required packages, and development tools.
- Learn the fundamentals of structuring a chatbot application and managing dependencies.
Module 3: Enterprise Data Sources and Document Ingestion
- Explore document sources such as PDFs, text files, knowledge articles, and business documentation.
- Learn document loading, text extraction, data cleaning, and metadata management.
Module 4: Document Chunking and Knowledge Preparation
- Understand chunking strategies, chunk size, overlap, and document segmentation.
- Prepare enterprise knowledge for efficient indexing and relevant information retrieval.
Module 5: Embeddings and Semantic Search
- Learn how text embeddings represent the semantic meaning of documents and queries.
- Explore similarity search and its role in retrieving contextually relevant information.
Module 6: Vector Databases and Indexing
- Understand vector storage, indexing, metadata filtering, and similarity search.
- Explore integrating vector databases such as Pinecone, Weaviate, Chroma, or Milvus, depending on project requirements.
Module 7: Large Language Model Integration
- Learn how to connect LLMs to chatbot applications through supported APIs or model services.
- Explore prompt design, context management, response generation, and model selection considerations.
Module 8: Designing the RAG Retrieval Pipeline
- Build an understanding of query processing, relevant document retrieval, and context assembly.
- Explore retrieval parameters, metadata filters, and methods for improving retrieval relevance.
Module 9: Enterprise Chatbot Architecture and Conversation Flow
- Design chatbot components for user interaction, retrieval, response generation, and application services.
- Explore conversation history, follow-up questions, context handling, and session management.
Module 10: Grounded Responses and Source Attribution
- Learn how retrieved enterprise information can be used to support chatbot responses.
- Explore source references, response validation, uncertainty handling, and strategies for reducing unsupported answers.
Module 11: Enterprise Data Security and Responsible AI
- Understand authentication, authorisation, data privacy, and access controls for enterprise chatbot systems.
- Explore prompt injection risks, sensitive information exposure, responsible AI, and human escalation requirements.
Module 12: Chatbot Testing and Performance Evaluation
- Evaluate retrieval relevance, answer correctness, contextual consistency, and response latency.
- Develop test questions and analyse failure cases to identify opportunities for improvement.
Module 13: API Integration and Application Deployment
- Understand how to expose chatbot functionality through APIs and integrate it with enterprise applications.
- Explore deployment environments, configuration management, logging, monitoring, and maintenance considerations.
Module 14: Capstone Project – Enterprise Knowledge Chatbot
- Design a chatbot that answers questions using an approved collection of enterprise documents.
- Implement document ingestion, indexing, retrieval, LLM integration, and response generation.
- Test the chatbot using representative questions and document its architecture, security considerations, evaluation results, and limitations through StepMerit's Building Enterprise Chatbots Using RAG training in Adelaide, Australia.

Exam details

Exam Level: Intermediate – Building Enterprise Chatbots Using RAG
Exam Type: Online assessment consisting of multiple-choice questions (MCQs) and scenario-based technical questions.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Passing Score: 70% (28 out of 40 questions).
Exam Language: English.
Practical Knowledge Assessment: Evaluate understanding of RAG chatbot architecture, document ingestion, chunking, embeddings, vector databases, LLM integration, retrieval workflows, and response validation.
Evaluation Criteria: Assess conceptual understanding, chatbot design decisions, retrieval workflow knowledge, troubleshooting skills, and awareness of enterprise security requirements.
Certification: StepMerit course-completion certificate, subject to applicable course and assessment requirements.
Exam Preparation: Review enterprise chatbot architecture, semantic search, vector indexing, prompt design, context management, response evaluation, API integration, and responsible AI practices.
Training and Assessment Support: StepMerit provides learning guidance and assessment preparation through its Building Enterprise Chatbots Using RAG training in Adelaide, Australia.

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Who this course is for

AI and Generative AI Developers Machine Learning Engineers Python Developers Large Language Model (LLM) Engineers RAG Application Developers Conversational AI Developers Chatbot Developers Software Engineers Data Engineers NLP Engineers Cloud and Application Developers AI Solutions Architects IT Professionals Exploring Enterprise Chatbot Development in Adelaide, Australia

Before you begin

Python Programming Fundamentals – Basic knowledge of Python syntax, functions, data structures, and packages is recommended for developing chatbot applications. Artificial Intelligence Fundamentals – Understand basic AI concepts, intelligent applications, and common business use cases. Large Language Model (LLM) Basics – Familiarity with LLMs, text generation, tokens, context windows, and conversational AI concepts. Generative AI Fundamentals – Understand how Generative AI creates responses and supports interactive applications. Retrieval-Augmented Generation (RAG) Fundamentals – Basic understanding of how external knowledge retrieval helps LLMs generate context-aware responses. REST APIs…

Learning outcomes

Problem Statement Telecommunication companies manage large volumes of customer enquiries related to mobile plans, broadband services, network connectivity, billing, roaming, SIM activation, troubleshooting, and service policies. Customer support teams often need to search multiple knowledge bases, technical documents, FAQs, and service manuals to provide accurate answers. This can lead to longer resolution times, inconsistent responses, repeated enquiries, and increased pressure on support agents. The challenge is to develop an intelligent enterprise chatbot that retrieves relevant information from approved telecom documents and generates context-aware responses while protecting customer…

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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 Experience: 5 years in software development, AI, application engineering, or related technical domains. Technical Training Experience: 150+ of technical training delivered. Candidates Trained: 180+learners trained in AI, software development, Generative AI, or related technologies. RAG and LLM Expertise: Knowledge of retrieval pipelines, context management, prompt design, LLM integration, and grounded response generation. Vector Database Skills: Experience with embeddings, semantic search, vector indexing, and relevant platforms such as Pinecone, Weaviate, Chroma, or Milvus, as applicable. Enterprise Chatbot Development: Understanding of conversational workflows, knowledge-base…

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Insights for Building Enterprise Chatbots using RAG

Learning insight 01

Building Enterprise Chatbots Using RAG: A Guide to Intelligent AI Chatbot Development

Organisations increasingly use artificial intelligence to improve customer support, simplify knowledge discovery, and automate information-based business workflows. However, traditional chatbots may struggle to answer complex questions that depend on internal documents, frequently updated policies, technical manuals…

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