Build reliable, production-ready Retrieval-Augmented Generation (RAG) applications with Claude.

This 2-day hands-on program covers the complete RAG lifecycle, from content preparation and embeddings to retrieval, reranking, grounded generation, evaluation, security, and monitoring. Participants build a practical RAG service that delivers answers from approved sources with verifiable evidence.

  • Why get trained: Learn content preparation, chunking, metadata, embeddings, vector storage, semantic and hybrid retrieval, reranking, context assembly, Claude citations, evaluation, and security.
  • Why it matters: Reliable RAG applications help organizations deliver accurate, traceable AI responses while improving information access and reducing unsupported outputs.
  • Who should attend: Software Developers, Application Engineers, AI Engineers, Data Engineers, Search Engineers, QA Automation Engineers, Test Architects, Technical Business Analysts, Solution Engineers, and Technical Leads.

Gain practical skills to engineer, evaluate, secure, and operationalize enterprise RAG applications with Claude. HRD Corp Claimable.

Overview

This two-day program teaches participants to build reliable retrieval-augmented generation applications with Claude. Participants implement the complete RAG lifecycle: use-case definition, content preparation, chunking, metadata, embeddings, retrieval, reranking, context construction, cited generation, evaluation, security, and operational monitoring.

This program focuses on retrieval application engineering, not autonomous agent orchestration. Participants build a production-oriented RAG service that answers from approved sources and produces verifiable evidence. Examples can be delivered in Python, TypeScript, C#, Java, or another supported enterprise stack; Python is not a mandatory prerequisite.

Skills Covered

  • Explain the RAG lifecycle and select an appropriate retrieval architecture
  • Prepare enterprise content using effective parsing, chunking, and metadata strategies
  • Generate and store embeddings using a suitable embeddings provider and vector store
  • Implement semantic, keyword, filtered, and hybrid retrieval
  • Apply query transformation, reranking, and context assembly techniques
  • Generate grounded answers with Claude citations or search-result attribution
  • Evaluate retrieval quality, groundedness, answer quality, latency, and cost
  • Secure and operationalize a retrieval-powered application

Prerequisites

You need working knowledge of one programming language and web APIs, a basic understanding of generative AI, prompts, and tokens, and familiarity with formats like JSON. Access to the Claude API and an approved development environment is also required.

Target Audience

  • Software developers and application engineers
  • AI engineers, data engineers, and search engineers
  • QA automation engineers and test architects
  • Technical business analysts, solution engineers, and technical leads

Course Curriculum

Module 1: RAG Architecture and Use Cases

RAG lifecycle, search versus generation, architecture options, limitations, and success measures.

Module 2: Content Ingestion and Preparation

Parsing, normalization, chunking, overlap, metadata, deduplication, versioning, and content quality.

Module 3: Embeddings and Indexing

Embedding concepts, model selection, vector stores, namespaces, indexing, and lifecycle management.

Module 4: Retrieval and Reranking

Semantic, lexical, hybrid, metadata-filtered, multi-query retrieval, reranking, and result diversity.

Module 5: Generation and Citations

Context assembly, prompt boundaries, search-result blocks, document citations, abstention, and structured answers.

Module 6: RAG Evaluation

Golden datasets, recall and ranking measures, groundedness, answer relevance, citation quality, and regression testing.

Module 7: Security and Operations

Authorization, tenant isolation, injection defense, sensitive data, caching, observability, latency, and cost.

Module 8: Capstone

uild, evaluate, and demonstrate a retrieval-powered application using an approved corpus.

Dates & Locations

Let’s make it work for you

Can’t find a date that fits? Need to train your whole team? Looking for a discount?
Speak to one of our learning experts today.

There’s no intakes scheduled for this course at the moment!

For enquiries, please contact our reps.

Exam & Certification

Complete the Retrieval-Augmented Generation (RAG) Application Engineering with Claude Training and receive your Retrieval-Augmented Generation Application Engineering with Claude Training Certificate of Completion after successfully completing the 2-day program. This validates your skills in designing and implementing reliable RAG applications, including content preparation, embeddings, retrieval, reranking, grounded generation, evaluation, security, and operational monitoring.

Training & Certification Guide

Why train with Trainocate

No. Labs can be adapted for Python, TypeScript, C#, Java, or an equivalent enterprise stack.

You will build a retrieval-powered assistant that retrieves authorized sources, provides citations, abstains when evidence is insufficient, and reports evaluation results.

The RAG lifecycle, content ingestion and chunking, embeddings and indexing, hybrid retrieval and reranking, cited generation, evaluation, and security and operations.

A laptop with a supported development environment and internet access, plus Claude API credentials. A vector store or approved local option and a sanitized sample corpus are used for exercises.

An architecture and data-flow diagram, an ingestion and indexing implementation, a retrieval and grounded-generation service, an evaluation dataset with scorecard, and a security and operations backlog.

Speak to a Training Consultant

All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631

Preferred mode of training
Checkboxes