Topic
rag
7 articles across 4 sub-topics
Sub-topic
4 articles
LLM Hallucinations: Causes, Detection, and Mitigation Strategies
TLDR: LLMs hallucinate because they are trained to predict the next plausible token ā not the next true token. Understanding the three hallucination types (factual, faithfulness, open-domain) plus the five root causes lets you choose the right mitiga...
Guide to Using RAG with LangChain and ChromaDB/FAISS
TLDR: 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 chunks at query time, and inject them into the LLM's ...
A Beginner's Guide to Vector Database Principles
TLDR: A vector database stores meaning as numbers so you can search by intent, not exact keywords. That is why "reset my password" can find "account recovery steps" even if the words are different. š Searching by Meaning, Not by Words A standard d...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: 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 into the prompt before generation. No retraining re...
Sub-topic
1 article
RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)
TLDR: RAG gives LLMs access to current knowledge at inference time; fine-tuning changes how they reason and write. Use RAG when your data changes. Use fine-tuning when you need consistent style, tone, or domain reasoning. Use both for production assi...
Sub-topic
1 article
LLM Evaluation Frameworks: How to Measure Model Quality (RAGAS, DeepEval, TruLens)
TLDR: š 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. DeepEval provides unit-test-style LLM evaluation....
Sub-topic
1 article
LangChain RAG: Retrieval-Augmented Generation in Practice
ā” 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 you build the full pipeline ā load ā split ā embed...