Home Events Retrieval-Augmented Generation (RAG) Systems Engineering

Retrieval-Augmented Generation (RAG) Systems Engineering

Description

This course provides a comprehensive, engineering-focused approach to designing and implementing Retrieval-Augmented Generation (RAG) systems for real-world applications. It covers how to combine Large Language Models with external knowledge sources to improve accuracy, reduce hallucinations, and enable context-aware reasoning. Participants will learn end-to-end RAG architectures, including document ingestion, embeddings, vector databases, retrieval strategies, and response generation. The course also explores advanced topics such as hybrid retrieval, multi-hop reasoning, and evaluation of RAG systems. Hands-on labs focus on building scalable, production-ready pipelines using modern frameworks and cloud services. Emphasis is placed on performance, relevance, security, and system observability. By the end of the course, participants will be able to engineer robust RAG systems for enterprise use cases.

 

🕒 Duration: 24 hours

👥 Target Audience:

  • Roles:
    AI Engineer, Data Engineer, Machine Learning Engineer, AI Solutions Architect

 

  • Seniority:
    Junior, Mid-Level
Webinar Content
Module 1: Foundations of RAG Systems
LLM Limitations & Motivation
 Introduction to RAG
  • What is RAG and why it is needed
  • LLM limitations (hallucinations, context limits)
  • Overview of RAG architecture

 

  • AI Practice: Use an LLM to compare answers with and without retrieval
RAG Architecture Deep Dive
  • Components: ingestion, embeddings, retrieval, generation
  • Data flow in RAG systems
  • System design considerations

 

  • AI Practice: Design a RAG architecture for a business use case
Module 2: Data Ingestion & Processing
Document Engineering
 Document Ingestion Pipelines
  • Data sources (PDFs, APIs, databases)
  • Parsing and preprocessing
  • Chunking strategies

 

  • AI Practice: Generate chunking strategies using AI
Embeddings & Vectorization
  • Embedding models
  • Semantic similarity concepts
  • Vector creation workflows

 

  • AI Practice: Compare embedding outputs for different texts
Module 3: Retrieval Systems
Vector Databases
 Vector Databases & Indexing
  • Intro to vector DBs (FAISS, Pinecone, etc.)
  • Indexing strategies
  • Similarity search

 

  • AI Practice: Query a vector store using AI-generated queries
Retrieval Strategies
  • Top-k retrieval
  • Filtering and ranking
  • Context window optimization

 

  • AI Practice: Tune retrieval parameters using AI suggestions
Module 4: Advanced Retrieval
Hybrid & Multi-hop
 Hybrid Retrieval
  • Combining keyword + semantic search
  • BM25 + embeddings
  • Re-ranking techniques

 

  • AI Practice: Generate hybrid search queries with AI
Multi-hop Reasoning
  • Complex query handling
  • Multi-step retrieval
  • Query decomposition

 

  • AI Practice: Use AI to break down complex questions into sub-queries
Module 5: RAG Integration
Application Layer
 RAG with APIs
  • Integrating RAG into backend systems
  • API design patterns
  • Response formatting

 

  • AI Practice: Build a RAG-powered API endpoint
Prompt Engineering for RAG
  • Context injection strategies- Prompt templates- Controlling hallucinationsAI

 

  • Practice: Optimize prompts for better RAG responses
Module 6: Evaluation & Optimization
Production Systems
 Evaluation Metrics
  • Accuracy, relevance, latency
  • Human vs automated evaluation
  • Benchmarking RAG systems

 

  • AI Practice: Evaluate RAG outputs using AI-generated criteria
Scaling & Capstone Project
  • Scaling RAG systems
  • Monitoring and observability
  • Final project implementation

 

  • AI Practice: Build and optimize a full RAG system
Learning Objectives:

After attending this webinar participants will be able to:

  • Design and implement end-to-end RAG architectures for real-world applications
  • Build and optimize document ingestion, embedding, and retrieval pipelines
  • Apply hybrid and advanced retrieval techniques to improve relevance and accuracy
  • Evaluate, monitor, and fine-tune RAG systems for production environments
  • Integrate RAG systems into APIs and intelligent applications
Prerequisite Knowledge
  • Experience with Python or .NET and familiarity with APIs and data processing
  • Basic understanding of machine learning concepts and LLM fundamentals

 

Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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