Chunking Strategies in RAG: How to Split Documents for Better Retrieval
Learn how chunking works in RAG systems, compare different chunking strategies, and understand how chunk size, overlap, and document structure affect retrieval quality.
6 docs tagged with "AI Systems"
View all tagsHow LLM Inference Works: From Prompt to Generated Tokens
Understand how LLM inference works, including tokenization, prefill and decode phases, KV cache, attention, sampling, and how transformer models generate text token by token in production systems.
Hybrid Search vs Dense Search in RAG: Which Retrieval Strategy Should You Use?
Compare hybrid search and dense search in RAG systems. Learn how semantic search, keyword search, BM25, and hybrid retrieval affect accuracy, recall, and production AI performance.
LLM Architecture Overview: How Modern Language Models Are Built
A system-level overview of LLM architecture, including transformer blocks, encoder vs decoder models, attention mechanisms, training and inference flow, and how all components fit together in modern AI systems.
Reranking in RAG Systems: Improving Retrieval Precision Before Generation
Learn how reranking improves RAG systems by refining retrieval results before generation. Compare cross-encoders, ColBERT, LLM rerankers, and production best practices.
What is RAG (Retrieval-Augmented Generation)?
Learn what Retrieval-Augmented Generation (RAG) is, how it works, why it improves LLM accuracy, and how it combines retrieval systems with large language models.