RAG
- Pages
- 7
- References
- 6
- Related Terms
- 4
Definition
RAG, or Retrieval-Augmented Generation, combines retrieved external information with generative model output.
Background
After the 2020 work by Lewis et al., RAG became a common design for grounding LLM answers in documents or knowledge bases.
Position
It sits next to Knowledge Graphs and memory systems as a way to feed external knowledge into LLM workflows.
Distinctions
- RAG is an architecture for retrieval plus generation, not a knowledge representation by itself.
- A Knowledge Graph structures knowledge; RAG retrieves information and uses it during generation.
Primary source-backed reference selected for this concept.
Page Context
- AI agent memory platform created with Graphiti and MCP
1. Executive Summary By combining Graphiti and MCP, AI agents can be provided with "memory that persists across conversation sessions" and "a standard point of contact from exte...
Quote: AI agent memory platform created with Graphiti and MCP ai-systems
- LLM Limits and Hallucinations: Factuality, Evaluation, and QA
LLM Limits and Hallucinations: Factuality, Evaluation, and QA 1. Executive Summary LLMs generate plausible text, but they are not truth checkers. Training optimizes next-token p...
Quote: LLM Limits and Hallucinations: Factuality, Evaluation, and QA ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
How LLM Training, Fine-Tuning, RAG, and Agents Differ 1. Executive Summary The most common mistake in LLM discussions is to treat pretraining, fine-tuning, prompting, RAG, tool ...
Quote: How LLM Training, Fine-Tuning, RAG, and Agents Differ ai-systems
- Research Log: LLM Limits, Hallucinations, and Evaluation Methods
Research Log: LLM Limits, Hallucinations, and Evaluation Methods Environment - model: gpt-5.4-mini - skill: research-report - prompt source: ops/codex/prompts/daily-issue-resear...
Quote: Research Log: LLM Limits, Hallucinations, and Evaluation Methods ai-systems
- Research Log: LLM Training, Fine-Tuning, RAG, and Agents
Research Log: LLM Training, Fine-Tuning, RAG, and Agents Environment - model: gpt-5.4-mini - skill: research-report - prompt source: ops/codex/prompts/daily-issue-research.md Re...
Quote: Research Log: LLM Training, Fine-Tuning, RAG, and Agents ai-systems
- LLM Limits and Hallucinations: Source Notes
- Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives - Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented...
Quote: LLM Limits and Hallucinations: Source Notes ai-systems
Pages
- AI agent memory platform created with Graphiti and MCP
A practical report that organizes the design theory, implementation decisions, and major alternative services for long-term memory that combines Graphiti's temporal knowledge graph and MCP.
ai-systems
- LLM Limits and Hallucinations: Factuality, Evaluation, and QA
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- How LLM Training, Fine-Tuning, RAG, and Agents Differ
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- Research Log: LLM Limits, Hallucinations, and Evaluation Methods
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- Research Log: LLM Training, Fine-Tuning, RAG, and Agents
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems
- LLM Limits and Hallucinations: Source Notes
This report explains why hallucinations happen from training objectives and inference behavior, compares benchmark evaluation with practical evaluation, and outlines what RAG, verification, citations, and tools can and cannot fix.
ai-systems
- LLM Training, Fine-Tuning, RAG, and Agents: Source Notes
A practical guide to the difference between pretraining, fine-tuning, prompting, RAG, tool use, and agents, framed by what each one changes and when to use it.
ai-systems