456
topic · #rag

Everything about rag

7 connected nodes across dictionary, workflows, comparisons, prompts, tool stacks and use cases.

/ Dictionary · 2
DEFDictionaryNODE·4E2543

Reranking

A second-pass model that reorders retrieved chunks by true relevance to the query.

#ai#rag
/rerankingopen →
DEFDictionaryNODE·6285C0

Chunking

Splitting documents into retrievable pieces before embedding them for RAG.

#ai#rag
/chunkingopen →
/ Comparisons · 1
VSComparisonNODE·2675FB

RAG vs Long Context Windows

Is RAG obsolete now that context windows are 1M+ tokens? Not quite — here's when each wins.

#ai#rag#architecture
/rag-vs-long-contextopen →
/ Prompts · 1
PRMPromptNODE·2B9242

RAG Answer With Strict Citations

Force the LLM to answer only from provided chunks and cite them by ID.

#rag#prompting
/rag-answer-with-citationsopen →
/ Tool Stacks · 3
STKTool StackNODE·45DC08

AI Support Agent Stack

Tier-1 support handled by an AI agent grounded on your docs, with human handoff.

#support#rag
/ai-support-agent-stackopen →
STKTool StackNODE·287DCB

RAG Context Enrichment Stack

A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.

#stack#rag#context-engineering
/rag-context-enrichment-stackopen →
STKTool StackNODE·417E90

Low-Cost RAG Stack

This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.

#rag#ai-cost-control#stack
/low-cost-rag-stackopen →