In the era of information explosion, businesses are faced with a data volume is growing, stretching from internal to customer data, and market. So how to medium business advantage is the huge amount of data, just optimized the operation process? Retrieval Augmented Generation (RAG) privacy is the groundbreaking solution to help solve the problem.
In the article below, let's Lac Viet learn RAG what is, how it works and 5 application highlights that business can apply.
1. Retrieval Augmented Generation (RAG) is what?
Retrieval Augmented Generation (RAG) is a method of combining information retrieval and module, student, content to create the natural feedback, accuracy by leveraging huge data warehouse to provide the most relevant information according to the context user.
In the context of the business need quick response to the customer request, RAG become effective tools to help optimize interaction between businesses and customers via chatbot system, faq, or the application support service.
Two main components of RAG
- Retrieval Module (module access): This department is responsible for searching, collecting data from the stock available information, such as the internal database, documentation, or online sources. This module ensures that the feedback is created always based on accurate information, as appropriate.
- Generation Module (module a): After the data is retrieved, this module will handle creating feedback using natural language through the language models, big (Large Language Models – LLM). The goal of the module is to create the coherent answer, bring personalization and user-friendly.
2. How the operation of Retrieval Augmented Generation
Retrieval system Augmented Generation (RAG) operate based on strict process consists of 5 main steps:
Step 1: collect data
The process first and most important in the Retrieval Augmented Generation is collecting data, because data is the foundation for the system to operate efficiently. The system retrieves data from various sources, including:
- Source business insider: Guidance documents, technical reports, databases, customer, email, or transaction history. This is the main data system important to help RAG correct answer queries related to internal operations.
- External source: Expert articles, industry research, forum or data publicly online. Data source this ensures the system can provide information that is time, comprehensive.
Up to date and correctc your data is a top priority to ensure the quality of feedback, help businesses always have reliable information to make decisions.
Step 2: divide data
After collection, data is classified, organized in small clusters to increase efficiency in the retrieval.
- Classification mechanism: Based on the factors such as theme, type of informationor features of use. For example, a business service provider, IT can split the data into categories such as “maintenance system”, “security solutions” and “technical support”.
- Results achieved: System building a data warehouse structure in which each data item is associated with the keyword or context respectively. This helps to significantly reduce the processing time when the query is sent to.
This division ensures the system to retrieve information quickly and at the same time limiting the risks to provide information not related.
Step 3: Embed document
Embed documents is an important step to transform text data into a form that the computer can understand and handle. This is done through the algorithm, deep learning (Deep Learning), which helps to convert text into performing the arithmetic (vector embeddings).
- Process operation: The language model as BERT, RoBERTaor Sentence Transformers used for semantic analysis of each text. These models ensure that data is represented correctly, optimized.
- Effect: Data after embed can be match semantics to the query of the user, regardless of the wording of them. For example, although users asking “how to fix the system?” or “fix IT how?”, system all understand this is the same intent and retrieve the relevant documents.
Thanks to this step, the Retrieval Augmented Generation has the ability to handle complex queries in a smoother way, bring valuable feedback high.
Step 4: Handling user queries
When a user submits a query, the system RAG perform two main tasks: semantic analysis and match information.
- Semantic analysis: Queries are converted into performing vector similar data've embedded, whereby the system to understand the real intention of the user. For example, if the query is “How to make server maintenance efficiency?”, RAG will recognize the keyword “maintenance” and “servers” to search for the matching documents.
- Match information: Based on performances vector, the system searches the data similarities in the repository and returns the most accurate results.
With the ability to handle natural language (Natural Language Processing – NLP), RAG not only answer questions but also provide additional information appropriate to help users have more comprehensive view of the issue.
Step 5: Create feedback with LLM
After retrieval of information, the last step is to use the model language (LLM) to create feedback. Model integrated information access with context query. Then create natural feedback, seamless, and consistent with the needs of users.
For example, If the user asked, “I need instructions on how to secure intranet system”the response from the Retrieval Augmented Generation can, including procedures for basic security and hints deployment of security tools modern.
LLM provides not only information but also adjust the wording to suit the style of communication of users, create and feel like you are interacting with a real expert.
3. RAG brings the benefits for business?
Retrieval Augmented Generation (RAG) not only is an advanced technology which is also strategic solutions for businesses that want to optimize workflow, enhance performance. Here are the benefits that RAG, help businesses not only overcome the challenges but also prevailed in a competitive environment:
- Increased efficiency in the processing of information: Processing huge amount of data efficiently, thereby improving work productivity. Instead of losing hours and hours to just, sort data, the system automatically retrieve the correct information in just a few seconds.
- Personalize customer experience: RAG doesn't just stop at answering questions, but also analysis of behavior, interests, and status of each customer to give feedback accordingly.
- Analyze and provide timely information: The system can access the reports, market analysis, information about the latest trend or historical data in just a snap. Help the management team make decisions based on real data, there are clear grounds.
- Increased processing speed problems: When in a crisis, businesses can rely on RAG to quickly identify the causes, and implementing solutions efficiently.
- Optimized hr: Instead of having a large team to handle information or answer the request from the client, the business can use the RAG to perform these tasks quickly, efficiently.
- Minimize errors: Traceability system and automatic feedback helps to eliminate the errors caused by humans, thereby avoiding the costs incurred are not necessary.
4. 5 outstanding application of Retrieval Augmented Generation
4.1 System faq advanced
System faq based on the RAG, not merely answer questions, but also give the accurate feedback, consistent with the context. Thanks to the ability to integrate data access and automatic feedback system, this is becoming an indispensable tool in an enterprise environment.
How to RAG operated in the faq:
When users take out of the question, Retrieval Augmented Generation, retrieving information from the database that are related and combined with context query. For example, if new employees need to learn the internal processes, the system will provide material, suitable explanation, help reduce the search time information.
4.2 Access to information quickly
One of the strong points of RAG is the ability to handle and provide information from a huge amount of data in just a snap. This is especially useful for large enterprises, where the data is stored in the warehouse complex.
Such in the health sector, the system can support, doctor, access medical records, treatment history in just a few seconds, help improve the efficiency of health care.
4.3 Improve the conversation with the Chatbot
Other with chatbot typically, the chatbot integrated Retrieval Augmented Generation have the ability to provide feedback, carries in-depth, rich in context, and even beyond the ability of the chatbot traditional.RAG not only based on the scenario programming available but also use real data to answer the query. For example, in the field of ecommerce chatbot can provide detailed information about the products accompanied by reviews from previous customers.
LV.ChatBotAI is tools app using artificial intelligence to support business optimization tasks in operations management. At the same time, LV.ChatBotAI also has the ability to integrate into the management software, the other to synthetic data, lookup, analyze, and make an assessment, prediction overview.
Virtual assistant answered 24/7 information internal business
LV.ChatbotAI support 24/7 to answer any policies/mode Financial accounting with any information, Questions & Answers thanks to the integrated platform ChatGPT, Gemini ...
- Answer all of the information from the Document with all contexts, instead of searching manually.
- Automatic synthesis of information to the user after a search in the Source data.
- Automatic summary when the question – and-answer on a document file in the archives Of quick test – test full, Read fast – Read enough to help comply with and carry out the process.
Support operational accounting
- Chatbot AI to answer any queries in real time right in functional statistics report help Leadership decisions quickly, reducing the time to explain the report.
- Tracking and analysis of financial indicators, warning fluctuations instant help businesses manage risks proactively.
- Automation scheduling, Email reminders when to term liabilities – payments, increased experience with Customers/suppliers.
- Financial forecast accurately with AI analysis, historical data, predict trends, help plan financial efficiency.
Optimal process lookup – approved
- Integrated in the management system documents, the register number, to help answer any queries in real time right in the workspace.
- The analysis of data, business optimization, management accounting, to digitize processes approved.
4.4 Create and summary of contents exactly
In the information age, the data processing huge volume of the information is succinct, easy to understand is a challenge. RAG business support, generate reports, summary of content quickly, saving time and cost.
For example in the field of journalism, reporters can use Retrieval Augmented Generation to summarize long articles into the main content, accessible to readers.
4.5 Support customer service
The ability to personalize customer feedback is the strong point of the RAG, to help businesses build trust, increase the satisfaction level of customers.
RAG helps chatbot not only understand but also feedback according to personal style, based on purchase history, behavior, access or emotional state of the customer. In addition, the system also supports expert sales consultants in the product description and propose solutions based on the individual needs of each customer.
Retrieval Augmented Generation not only is an advanced technology, but also as a powerful tool to help businesses optimize the ability to access information, improve customer experience, increase efficiency in internal operations. With the ability to mix between the search data and model language (LLM), RAG brings the groundbreaking solution, from system faq advanced to automate content, support customer service. Not to be left behind, the businesses need to actively capture al, deployment of modern technologies such as RAG, open up development opportunities durable advice, improve operational efficiency in the future.
CONTACT INFORMATION:
- Lac Viet Informatics Joint Stock Company
- Hotline: 0901 555 063 | (+84.28) 3842 3333
- Email: info@lacviet.vn – Website: https://lacviet.vn
- Headquarters: 23 Nguyen Thi Huynh, P. 8, Q. Phu Nhuan, ho chi minh CITY. Ho Chi Minh
