Topic
Rag
Learn Rag as a connected topic across chapters, concepts, simulations, and interview reasoning.
10 Concepts29 Articles8h 38m
Overview
Learn Rag as a connected topic across chapters, concepts, simulations, and interview reasoning.
How this topic helps
System Design
Distributed Systems
Llm
Interview Prep
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Articles
29 matched articles
Article 1RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)📌 TL;DR Summary
Use RAG when facts change frequently and answers must be source-grounded.
Use fine-tuning when you need stable behavior: tone, format, and domain-specific reasoning.
Use RAG + fine-t31 minArticle 2LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)TLDR: 📏 Traditional ML metrics (accuracy, F1) fail for LLMs because there's no single "correct" answer. RAGAS measures RAG pipeline quality with faithfulness, answer relevance, and context precision.16 minArticle 3LangChain RAG: Retrieval-Augmented Generation in Practice⚡ TLDR: RAG in 30 Seconds
TLDR: RAG (Retrieval-Augmented Generation) fixes the LLM knowledge-cutoff problem by fetching relevant documents at query time and injecting them as context. With LangChain 19 minArticle 4Guide to Using RAG with LangChain and ChromaDB/FAISSTLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chu14 min
Article 5RAG Explained: How to Give Your LLM a Brain UpgradeTLDR: LLMs have a training cut-off and no access to private data. RAG (Retrieval-Augmented Generation) solves both problems by retrieving relevant documents from an external store and injecting them i11 minArticle 6ANN Index Types Explained: When to Choose Flat, HNSW, IVF, or IVF-PQTLDR: If your dataset is small and correctness is critical, use Flat. If you need high recall with low latency and enough RAM, use HNSW. If your corpus is huge and memory is your bottleneck, use IVF-P14 minPage 1 of 5