RAG vs KAG: Comparison and Differences in GenAI Knowledge Augmentation Generation

In a rapidly evolving landscape in the field of natural language processing (NLP) and AI systems, two methodologies are gaining prominence: RAG (retrieval-augmented generation) and KAG (knowledge-augmented generation).

These two approaches enhance the capabilities of linguistic models by integrating external knowledge sources but differ in how they access and use knowledge.

We have compiled their most important points to consider to compare them and analyze their architectures, use cases, advantages, etc.

What is RAG?

RAG, or Retrieval-Augmented Generation, is a framework that combines the power of retrieval and generation-based models. It has become one of the most important applications of generative AI, which connects external documents (PDFs, videos, etc.) to LLM for Q&A cases.

The operation of RAG is based on two pillars:

• Retrieval: a query goes through a retrieval system, which retrieves relevant documents or passages from an external knowledge source.
• Generation: these retrieved passages are incorporated as context to a generative model (such as GPT-4 or Gemini) and the model synthesizes the information to generate a relevant answer.

Its applications are varied and range from answering open domain queries or chatbots that require access to up-to-date or domain-specific information, to customized search engines.

Its main advantages are that it combines the strengths of retrieval (precision) and generation (natural language fluency) and reduces “hallucination”, as it bases its results on the retrieved data. This results in dynamic access to knowledge, better contextual responses, or efficient knowledge integration.

GraphRAG, which improves RAG retrieval by analyzing and creating knowledge graphs, was recently introduced. Still, some limitations need to be addressed, and thanks to KAG (Knowledge Augmented Generation), many of these problems can be overcome.

What is KAG?

KAG or Knowledge-Augmented Generation presents a hybrid approach that enhances the generative capabilities of language models by directly incorporating structured knowledge graphs or external knowledge bases into the model architecture.

Unlike RAG, which retrieves unstructured data, KAG focuses on the integration of structured knowledge to improve the quality of generation. It is based on the OpenSPG engine and addresses the limitations of traditional question-and-answer systems.

Its key components are:

• Logical reasoning: supports advanced reasoning capabilities, such as multi-step reasoning, allowing you to connect and infer answers from multiple pieces of related information.
• Domain-specific knowledge: It is designed for vertical knowledge bases, which makes it work well in domains that require deep and specialized knowledge. In addition, it integrates structured and unstructured data into a unified system.
• Improved accuracy: reduces errors and provides clearer and more accurate answers.
• Knowledge graph integration: can incorporate domain-specific schemas and rules, making it adaptable to different professional needs, from answering easy questions to reasoning in complex scenarios.
• Customization: can incorporate domain-specific schemas and rules, making it adaptable to different professional needs.

Its major advantages range from structured knowledge or improved accuracy for fact-based questions, to consistent and less error-prone answers.

However, it is still limited to the inherent knowledge encoded in the knowledge graph, scalability challenges, or dependence on the quality of the knowledge graph.

How does KAG work?

We can summarize the functioning of KAG as a two-step process based on learning and response:

1. Learning: KAG takes all the documents, data, or knowledge provided to it and breaks them down into smaller, more meaningful chunks. It then identifies important pieces of information (name, dates, relationships, or facts) and builds a knowledge map, a web of connected ideas.
2. Respond: when asked a question, KAG understands what is being asked and can rewrite the question to make it clearer (if necessary). From here, it searches the knowledge graph to find the most relevant information, and reasons and connects multiple pieces of information to give the complete answer. And, finally, it brings it all together into a clear, human-like answer.

RAG vs KAG: Key Differences

Both RAG and KAG present state-of-the-art approaches to enhance the capabilities of generative models, but are suitable for different types of tasks.

RAG excels in open-domain tasks, where dynamic and unstructured data needs to be retrieved and synthesized. KAG, on the other hand, is more effective in scenarios requiring structured and factual information from knowledge graphs.

In addition, they differ in the following:

Use of knowledge graph

RAG or GraphRAG uses a general knowledge graph for retrieval but lacks deep reasoning.

KAG, on the other hand, constructs domain-specific knowledge graphs and uses advanced reasoning to interpret the information.

Reasoning capabilities

RAG retrieves data, but has difficulty combining and using it in complex queries.

KAG uses multi-hop reasoning to connect and synthesize information to obtain accurate answers.

Handling complex queries

RAG is very effective for simple queries but can miss the big picture. Whereas KAG excels at complex, domain-specific queries by breaking them down and synthesizing the answers.

Accuracy

GraphRAG has improved precision but is still prone to errors in complex queries. In contrast, KAG offers professional-level accuracy by combining retrieval, reasoning, and graph alignment.

The choice between the two will depend largely on the type of data you are working with and the nature of the task at hand. For general-purpose applications that require retrieving and generating answers based on a wide variety of documents, RAG is usually the best choice. However, for tasks that require consistent, fact-based answers based on structured knowledge, KAG offers a more reliable approach.

Both methods continue to evolve and will become even more important in the creation of more powerful and accurate AI systems in the future.