What is RAG? Graphic teaching and practical examples that novices understand

RAG is the abbreviation of "Retrieval-Augmented Generation", which can be translated as "retrieval-Augmented Generation" in Chinese. It is a new technology that combines language models and knowledge databases, allowing AI to "check information" when answering questions and then generate content 。

Why do you need RAG? Because traditional language models can only rely on the knowledge base during training, they are prone to "hallucination", that is, fabricating error information. RAG can instantly find information from external databases, making the answers more accurate and instant.

Taking bank customer service as an example, a traditional AI model may cause incorrect answers when a customer asks questions like "When did I pay my bill last month?" because of the inability to instantly access personalized account information or the latest system records. Using RAG technology, AI can first search instantly from the bank's internal knowledge base or personal account records, and then give correct answers, significantly reducing error rates and improving customer experience . Simple metaphor: RAG is like an AI that opens the notes before the exam, it is equally smart but more reliable.

To understand how RAG works, we can imagine that AI is like a student who was taking an exam. In the past, he could only answer based on memory, but now he can open reference materials to help answer questions.

The RAG workflow is divided into three simple steps:

1. Retrieval: When you ask a question, the AI ​​will first look for relevant information, just like looking for answers through a book. These materials may come from websites, PDFs, or internal documents.
2. Integration: The information found will be sorted out and handed over to the AI ​​as prompt content to let it understand the background information.
3. Generation: Finally, AI produces answers based on these materials, just like students write a complete answer based on references.

In this way, AI will not talk out of thin air, but will be able to find the information and answer it after checking out the information, which is more accurate and more reasonable. 。

RAG is not just a technology, it is also a way to help AI become smarter and more practical. When we face work that requires accurate information, such as answering customer questions, sorting corporate information, or writing reports, traditional language models may give wrong answers because the information is outdated or not comprehensive enough. . At this time, RAG played an important role. Here are three main benefits it brings:

1. More accurate answers: Traditional AI models can only rely on data from training, but these data may have been outdated long ago. RAG can instantly find new information, just like students have the latest version of reference books available during the exam, which naturally gives more accurate answers.
2. Rarely reduce hallucination rates: Many AI models sometimes talk randomly because they guess by memory. The advantage of RAG is "check the information first and then answer the answer", just like checking the information before doing homework, so you will not write it wrong.
3. Easy to update and maintain: Traditional models need to add new knowledge, and you have to practice it all again; and RAG just needs to update the content of the database, as simple as changing to a new reference book, saving time and effort.

These advantages are particularly suitable for industries that require precise knowledge, such as finance, medical care, customer service, etc.

After understanding how RAG works and its advantages, you may be curious: What is different from the language models we are familiar with (such as ChatGPT)? Here we have compiled a comparison table to help you quickly compare the differences between RAG and traditional language model (LLM)

It can be said that RAG is an advanced version that makes LLM more practical, but it also means that more infrastructure and data governance is needed. Before importing, enterprises should evaluate their own technical capabilities and maintenance costs.

RAG is not just a theoretical technology, it has been widely used in many industries, helping companies save time, reduce errors and improve customer experience. Here are several common application scenarios and examples to show you how it can benefit in the real world:

• Customer service robot: Combining the company's knowledge base and FAQ, customer service AI can quickly answer various questions and even learn new product information instantly . For example, bank customer service can integrate internal regulations and transaction records to assist customers in inquiring information such as bills, credit card limits, loan progress, etc., save manpower and improve accuracy.
• Internal knowledge query system: Employees can search SOP, contract, and project information through natural language. For example, when employees in manufacturing industry enter the "machine abnormality exclusion process", the system can instantly find relevant technical documents to reduce the risk of error operations.
• Legal/Medical Assistance System: Help professionals quickly search articles or research reports and organize abstracts for reference . The lawyer can enter "The latest Consumer Protection Law amendment content", and the RAG system will help him find the key updates and list the comparison table.
• Education assistant: Provide additional explanations for the content of the textbook, or allow students to obtain reference materials when asking questions. For example, the teacher talked about the "principles of volcanic eruptions" in class. After students enter the problem, the system can retrieve clear graphic and text descriptions from textbooks, learning web pages, and homemade textbooks.

GenApe AI through Personalized space Use RAG technology to create an AI assistant for enterprises, so that AI can understand internal information, automatically respond to questions, or generate article content, and is widely used in customer service, training, engineering support and other scenarios.

For example, a company imported GenApe's RAG solution to assist in writing ESG reports. In the past, it required a lot of manpower and manual integration of a large number of policies and data. After importing, AI can instantly retrieve various sustainable development indicators and implementation content from the internal database, and then automatically generate the first draft, greatly reducing labor and time costs. , the writing efficiency has been improved by more than 60%.

Click here to experience RAG for free now → https://app.genape.ai/text-to-image

Experience GenApe now and let AI save you more time!

Whether you are a business owner, project manager, or an internal knowledge management leader, GenApe can help you organize complicated information easier. Experience in person how AI can help you accelerate content production and improve team efficiency.

RAG is the abbreviation of "Retrieval-Augmented Generation", which can be translated as "retrieval-Augmented Generation" in Chinese. It is a new technology that combines language models and knowledge databases, allowing AI to "check information" when answering questions and then generate content 。

Why do you need RAG? Because traditional language models can only rely on the knowledge base during training, they are prone to "hallucination", that is, fabricating error information. RAG can instantly find information from external databases, making the answers more accurate and instant.

Taking bank customer service as an example, a traditional AI model may cause incorrect answers when a customer asks questions like "When did I pay my bill last month?" because of the inability to instantly access personalized account information or the latest system records. Using RAG technology, AI can first search instantly from the bank's internal knowledge base or personal account records, and then give correct answers, significantly reducing error rates and improving customer experience . Simple metaphor: RAG is like an AI that opens the notes before the exam, it is equally smart but more reliable.

To understand how RAG works, we can imagine that AI is like a student who was taking an exam. In the past, he could only answer based on memory, but now he can open reference materials to help answer questions.

The RAG workflow is divided into three simple steps:

1. Retrieval: When you ask a question, the AI ​​will first look for relevant information, just like looking for answers through a book. These materials may come from websites, PDFs, or internal documents.
2. Integration: The information found will be sorted out and handed over to the AI ​​as prompt content to let it understand the background information.
3. Generation: Finally, AI produces answers based on these materials, just like students write a complete answer based on references.

In this way, AI will not talk out of thin air, but will be able to find the information and answer it after checking out the information, which is more accurate and more reasonable. 。

RAG is not just a technology, it is also a way to help AI become smarter and more practical. When we face work that requires accurate information, such as answering customer questions, sorting corporate information, or writing reports, traditional language models may give wrong answers because the information is outdated or not comprehensive enough. . At this time, RAG played an important role. Here are three main benefits it brings:

1. More accurate answers: Traditional AI models can only rely on data from training, but these data may have been outdated long ago. RAG can instantly find new information, just like students have the latest version of reference books available during the exam, which naturally gives more accurate answers.
2. Rarely reduce hallucination rates: Many AI models sometimes talk randomly because they guess by memory. The advantage of RAG is "check the information first and then answer the answer", just like checking the information before doing homework, so you will not write it wrong.
3. Easy to update and maintain: Traditional models need to add new knowledge, and you have to practice it all again; and RAG just needs to update the content of the database, as simple as changing to a new reference book, saving time and effort.

These advantages are particularly suitable for industries that require precise knowledge, such as finance, medical care, customer service, etc.

After understanding how RAG works and its advantages, you may be curious: What is different from the language models we are familiar with (such as ChatGPT)? Here we have compiled a comparison table to help you quickly compare the differences between RAG and traditional language model (LLM)

It can be said that RAG is an advanced version that makes LLM more practical, but it also means that more infrastructure and data governance is needed. Before importing, enterprises should evaluate their own technical capabilities and maintenance costs.

RAG is not just a theoretical technology, it has been widely used in many industries, helping companies save time, reduce errors and improve customer experience. Here are several common application scenarios and examples to show you how it can benefit in the real world:

• Customer service robot: Combining the company's knowledge base and FAQ, customer service AI can quickly answer various questions and even learn new product information instantly . For example, bank customer service can integrate internal regulations and transaction records to assist customers in inquiring information such as bills, credit card limits, loan progress, etc., save manpower and improve accuracy.
• Internal knowledge query system: Employees can search SOP, contract, and project information through natural language. For example, when employees in manufacturing industry enter the "machine abnormality exclusion process", the system can instantly find relevant technical documents to reduce the risk of error operations.
• Legal/Medical Assistance System: Help professionals quickly search articles or research reports and organize abstracts for reference . The lawyer can enter "The latest Consumer Protection Law amendment content", and the RAG system will help him find the key updates and list the comparison table.
• Education assistant: Provide additional explanations for the content of the textbook, or allow students to obtain reference materials when asking questions. For example, the teacher talked about the "principles of volcanic eruptions" in class. After students enter the problem, the system can retrieve clear graphic and text descriptions from textbooks, learning web pages, and homemade textbooks.

GenApe AI through Personalized space Use RAG technology to create an AI assistant for enterprises, so that AI can understand internal information, automatically respond to questions, or generate article content, and is widely used in customer service, training, engineering support and other scenarios.

For example, a company imported GenApe's RAG solution to assist in writing ESG reports. In the past, it required a lot of manpower and manual integration of a large number of policies and data. After importing, AI can instantly retrieve various sustainable development indicators and implementation content from the internal database, and then automatically generate the first draft, greatly reducing labor and time costs. , the writing efficiency has been improved by more than 60%.

Click here to experience RAG for free now → https://app.genape.ai/text-to-image

Experience GenApe now and let AI save you more time!

Whether you are a business owner, project manager, or an internal knowledge management leader, GenApe can help you organize complicated information easier. Experience in person how AI can help you accelerate content production and improve team efficiency.