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Articles on RAG (Retrieval Augmented Generation), AI knowledge systems and document intelligence. Author: Vaibhav
RAG series
RAG Introduction: What Is Retrieval Augmented Generation?
A complete guide to RAG. What it is, why it matters and how it connects retrieval with language models.
Start hereWhat Is RAG? Definition and Core Concepts
Clear definitions of RAG, retrieval, augmentation and generation. Perfect starting point for beginners.
RAG vs Fine-Tuning: When to Use Which
Compare RAG and fine-tuning. Use cases, trade-offs and how to choose the right approach.
RAG Architecture: Components and Data Flow
How a RAG system is built. Indexing, retrieval and generation pipeline explained.
Embeddings in RAG: How Text Becomes Vectors
What embeddings are, how they represent meaning and why they matter for retrieval.
Vector Databases for RAG: Storage and Search
Vector DBs, similarity search and how to store and query embeddings at scale.
Chunking Strategies for RAG: Splitting Your Documents
How to split documents for RAG. Chunk size, overlap, semantic and sentence-based chunking.
Retrieval Methods in RAG: Dense, Sparse and Hybrid
Dense vs sparse retrieval, hybrid search, reranking and when to use each.
Evaluating RAG Systems: Metrics and Practices
How to measure RAG quality. Faithfulness, relevance, latency and evaluation frameworks.
Production RAG: Best Practices and Deployment
Operating RAG in production. Monitoring, caching, fallbacks and reliability.
All posts credit the author: https://vaibhav.co.uk/
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