A comprehensive technical handbook on Retrieval-Augmented Generation (RAG) engineering, covering architecture, vector databases, embedding strategies, retrieval optimization, LLM integration, evaluation, and production deployment.
Chapters
LLM Fundamentals for RAG Engineers
20 min read
Core RAG Architecture Patterns
18 min read
Data Ingestion and Processing Pipelines
22 min read
Deep Dive into Embedding Models
25 min read
Vector Databases: Concepts and Selection
20 min read
Indexing Strategies for Efficient Retrieval
18 min read
Semantic Search vs. Keyword Search
15 min read
Implementing Hybrid Search Systems
22 min read
Advanced Retrieval Techniques and Re-ranking
25 min read
Context Window Management and Prompt Engineering
20 min read
Managing Multi-Modal RAG Pipelines
22 min read
Evaluating RAG Pipelines: Metrics and Frameworks
25 min read
Guardrails and Trustworthiness in RAG
18 min read
Scaling RAG Systems for Production
20 min read
Monitoring and Observability for LLM Applications
22 min read
Security and Privacy in Enterprise RAG
20 min read
CI/CD and DevOps for Generative AI
18 min read
Fine-Tuning vs. RAG: When to Use Which
20 min read
The RAG Engineer Career Roadmap
15 min read