Description:

Retrieval-Augmented Generation (RAG) is a powerful architecture for grounding large language models in trusted data—but building a RAG system that works reliably in production requires more than connecting a model to a vector database.

This course focuses on the practical best practices that separate experimental prototypes from enterprise-grade AI systems. Participants will learn how to improve retrieval accuracy, reduce hallucinations, optimize chunking strategies, design effective prompts, implement evaluation frameworks, and build guardrails that protect data integrity and user trust. Through architecture reviews, real-world case studies, and guided exercises, you will learn how to design RAG pipelines that are scalable, cost-efficient, secure, and measurable. At the end of the workshop, you will be equipped with a practical checklist and reference architecture for building high-quality RAG systems in production environments

Duration:

Half Day

Course Code: BDT 539

Learning Objectives:

After this course, you will be able to:

1. Diagnose common failure modes in RAG systems
2. Design effective chunking and embedding strategies
3. Improve retrieval precision and reduce hallucinations
4. Implement evaluation and monitoring frameworks
5. Apply guardrails and security best practices

AI Practitioners, Solution Architects, Technical Product Managers, Data Teams, and Developers who have basic experience with Retrieval-Augmented Generation and want to improve reliability, scalability, and performance.

Basic understanding of LLMs and familiarity with RAG concepts. Experience with any RAG tool (n8n, LangChain, LlamaIndex, etc.) is helpful but not required.

Course Outline:

1. Foundations: What Makes a RAG System “Good”?
   1. Quick recap of RAG architecture (Retriever + Generator + Knowledge Base)
   2. Common failure modes: hallucination, poor recall, irrelevant retrieval
   3. Precision vs. Recall trade-offs in business applications
   4. Lab: Reviewing and diagnosing a flawed RAG pipeline

2. Document Processing & Chunking Strategy
   1. Why chunking strategy impacts retrieval quality
   2. Chunk size, overlap, and semantic boundaries
   3. Metadata enrichment and filtering techniques
   4. Lab: Comparing chunking strategies for the same dataset

3. Retrieval Optimization & Embedding Strategy
   1. Embedding model selection considerations
   2. Hybrid search (semantic + keyword)
   3. Reranking strategies for improved relevance
   4. Top K tuning and similarity thresholds
   5. Lab: Tuning retrieval parameters for improved accuracy

4. Prompt Engineering & Grounding Techniques
   1. Structuring system prompts for grounded responses
   2. Context injection strategies
   3. Citation enforcement and source transparency
   4. Handling out-of-scope and low-confidence queries
   5. Lab: Designing a “grounded” prompt template

5. Evaluation, Guardrails & Production Readiness
   1. Offline vs. online evaluation methods
   2. Creating test datasets for RAG validation
   3. Monitoring drift and retrieval degradation
   4. Security, privacy, and data governance considerations
   5. Cost optimization and scalability strategies
   6. Lab: Building a RAG Best Practices checklist for your organization

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to set up n8n and API keys.