Understanding Retrieval-Augmented Generation (RAG) - Concepts, Paradigms, and Applications
What is RAG?
RAG means Retrieval-Augmented Generation. It is a kind of technique that integrates information retrieval and natural language generation.
Why is RAG?
Large Language Models(LLMs) have powerful ability to generate human-like language and perform well in processing natural language. However, due to their training data, they can only respond to the knowledge before they are trained and fail to respond to real time queries. For example, if you ask deepseek-v3 how the weather is in NanJing today, he will tell you that his knowledge base ends in July 2024. However, if you enable web search function, he will tell you about the weather correctly.
This is the challenge that RAG tries to solve — the ability of LLMs to interact with real-time information, such as knowledge bases, APIs or the web. It bridges the gap between static training data and dynamic knowledge base.
What makes up a RAG?
RAG has 3 main parts: Retrieval, Augmentation and Generation.
- Retrieval means querying external database including knowledge bases, APIs and webs, and find the most relevant information snippets.
- Augmentation means processing retrieved data by extracting and summarizing the most relevant information snippets to align with the query context.
- Generation means combining the retrieved information with user’s query to produce relevant and reliable responds.
What are the RAG paradigms?
Currently, there are 5 RAG paradigms, which are Naïve RAG, Advanced RAG, Modular RAG, Graph RAG and Agentic RAG.
Here is a comparison of the different RAG paradigms.
| Paradigms | Pros (Features) | Cons (Limits) | Algorithms |
|---|---|---|---|
| Naïve RAG | - Simple and easy to implement - Suitable for fact-based queries | - Lack of contextual awareness - Fragmented outputs - Scalability issues | - Simple keyword-based retrieval techniques, such as TF-IDF and BM25 |
| Advanced RAG | - High precision retrieval - Improved contextual relevance | - Computational overhead - Limited scalability | - Dense retrieval models (e.g., DPR) - Neural ranking and re-ranking - Multi-hop retrieval |
| Modular RAG | - High flexibility and customization - Suitable for diverse applications - Scalable | - Increased complexity - Requires careful design and tuning | - Hybrid retrieval (sparse and dense) - Tool and API integration - Composable, domain-specific pipelines |
| Graph RAG | - Relational reasoning capabilities - Mitigates hallucinations - Ideal for structured data tasks | - Limited scalability - Data dependency - Complexity of integration | - Integration of graph-based structures - Multi-hop reasoning - Contextual enrichment via nodes |
| Agentic RAG | - Adaptable to real-time changes - Scalable for multi-domain tasks - High accuracy | - Coordination complexity - Computational overhead - Limited scalability | - Autonomous agents - Dynamic decision-making - Iterative refinement and workflow optimization |
What are the limitations of traditional RAG systems?
- Contextual Integration: Inability to effectively link retrieved information.
- Multi-step Reasoning: Inability to refine answers based on intermediate understanding or user feedback when answering difficult questions.
- Scalability and Latency Issues: An increase in the amount of data can greatly increase the computation of querying and ranking.
What are the applications of RAG?
- Customer support and virtual assistants
- Healthcare and personalized medicine
- Legacy and contract analysis
- Finance and risk analysis
- Education and personalized learning
- Graph-enhanced applications in multimodal workflows