RAG (Retrieval Augmented Generation) is what? Pattern RAG works?

In the era of information explosion, businesses are faced with a data volume is growing, stretching from internal to customer data, 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 Computing learn RAG is what? How does it work? The app highlights that businesses can apply.

1. RAG what is?

RAG or Retrieval Augmented Generation 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 collect data from the stock available information such as the internal database, documentation or other online sources. This module ensures that the feedback is created always based on accurate information suitable.
• 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 individualised user friendly.

2. RAG overcome the limitations of the model WHO traditional

The model Generative AI tradition (as ChatGPT), based primarily on data that has been trained earlier encounter many limitations when applied in practice:

• Error “Hallucination” (hallucinations information): WHO created the information is not true or not in the training data. For example: When the chatbot was asked about a new product launch recently, it can provide false information or even fabricated information does not exist.
• Misinformation or outdated: ONE based only on the training data from specific time can't be updated with new information if not coach again. For example: A trainer in 2021 could not know about the event or change in policies, business takes place in the year 2024.
• Can't check the source information: Do not cite the source data when the reply, users can hardly verify the correctness of the information. For example: In the field requires transparency as finance or legal, not knowing information is based on sources which is a big risk.

Retrieval Augmented Generation overcome these limitations by combining the ability to access new information correct from data sources external or internal to ensure the answer is not only true but also can be derived.

For example: Suppose a business provides services to support customers using chatbot AI. When customers ask:

“Warranty policy of the products X what is the latest?”

• WHO traditional may answer based on old information or give answers general, do not update.
• RAG will retrieve the information from the official database of the company to provide detailed answers updated: “Product X have warranty policy latest is 12 months, applicable from the date 1/1/2024 include replace parts for free in case of technical errors.”

3. How the activities of RAG Retrieval Augmented Generation

Retrieval system Augmented Generation (RAG) operate based on strict process consists of 5 main steps:

• Step 1: collect data
• Step 2: divide data
• Step 3: Embed document
• Step 4: Handling user queries
• Step 5: Create feedback with LLM

Step 1: collect data

The first process most important in the Retrieval Augmented Generation is collecting data by data privacy 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.

Calculator updates, main campusc your data is a top priority to ensure the quality of feedback to 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 information or features of use. For example, a business service provider, IT can split the data into categories such as “maintenance system”, “security solution” or “technical support”.
• Results achieved: System building a data warehouse structure in which each data item is associated with the keyword or context corresponds help to significantly reduce the processing time when the query is sent to.

The division ensures Retrieval system Augmented Generation access 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) to help convert text into performing the arithmetic (vector embeddings).

• Process operation: The language model as BERT, RoBERTa or Sentence Transformers used for semantic analysis of each text segment to ensure 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, whether users asking “how to fix the system?” or “fix IT how?” the system all understand this is the same intent and retrieve the relevant documents.

Thanks to this step, RAG capable of handling complex queries in a smoother way to bring valuable feedback high.

Step 4: Handling user queries

When a user submits a query, the system RAG perform two main tasks: semantic analysis, match information.

• Semantic analysis: the Query is 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, 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 match the communication style of the user who created the feeling like you are interacting with a real expert.

4. 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 bring 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, status of each customer to give feedback accordingly. 
• Analysis provides 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 have clear base.
• 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 effective.
• Minimize errors: System access, auto-response eliminates the human error caused from it to avoid the costs incurred are not necessary.

5. 5 outstanding application of Retrieval Augmented Generation

Applying RAG not only bring the flexibility, accuracy in information processing, but also open up many innovative solutions for the business operations.

Here are 5 apps highlights of RAG to help optimize workflow, increase operational efficiency.

• System faq advanced
• Access quick information
• Improved conversation with Chatbot
• Create and summary of contents exactly
• Support customer service

5.1 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 auto-response, these systems are 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 relevant in combination with the 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.

5.2 Access to information quickly

One of the strong points of RAG is capable of handling provide information from a huge amount of data in just a snap 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 helps improve the efficiency of health care.

5.3 Improve the conversation with the Chatbot

Other with chatbot typically, the chatbot integrated RAG ability to provide feedback, carries in-depth, rich in context, 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. Such in the field of ecommerce chatbot can provide detailed information about the products accompanied by reviews from previous customers.

Lac Viet Chatbot AI Assistant is a tools app using artificial intelligence to support business optimization tasks in operations management. At the same time, Vietnam Chatbot AI Assistant also has the ability to integrate into the management software, the other to synthetic data, research, analysis, assessments, predict overview.

Virtual assistant answered 24/7 information internal business

Lac Viet Chatbot AI Assistant 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.
• Auto-summary information when questions and answers on a document file in the archives Of quick test test full, read fast read enough help compliance implementation 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.

5.4 Create content summary accurate

In the information age, the data processing huge volume of the condensed information understandable is a challenge. RAG business support, generate reports, content summary, quick help save time 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.

5.5 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, the proposed solution is based on the individual needs of each customer.

6. Process deployment RAG for business

The application of Retrieval Augmented Generation (RAG) into business operations not only in technology but also as an overall strategy, which should be done in a basically scientific. To ensure the system RAG bring practical value businesses need to comply with a deployment process clearly effective.

Here are 5 basic steps to help businesses optimize the integrated RAG into the internal system.

• Step 1: Identify needs, goals of the business
• Step 2: Prepare data base and model WHO
• Step 3: integrate RAG into the current system
• Step 4: testing efficiency rating
• Step 5: optimal scaling

Step 1: Determine the needs and goals of the business

To deploy successful Retrieval Augmented Generationthe first step is to clearly define the target demand of the business. This helps to ensure that the solution RAG matching strategy, bringing optimal efficiency.

Analysis of specific needs:

• Enterprises need to improve the customer experience through chatbot?
• Need support staff lookup internal information quickly?
• Need to increase the accuracy for the report automatically?
• Need to enhance the ability to query data in real time?

Define clear goals:

• Discount 30% response rate deviations from chatbot customer support.
• Increase the search speed, internal documents up 50%.
• Ensure the AI system provides information from the data source is updated.

Step 2: Prepare data base and model WHO

The deployment RAG require business fully prepared input data and models WHO available to integrate effective.

• Determine the type of data: Text data from internal documents, technical manuals, FAQs, information products/services, customer data,...
• Standardize – cleaning data: Remove outdated data or incorrect. Ensure data clearly structured to WHO can access easily.
• Prepare model WHO is there: Specify the language models are used (for example, GPT-3, GPT-4, or the custom model). Evaluate the possibility of integration of models with features to retrieve data.

Step 3: integrate RAG into the current system

When prepared the database and model WHO, the next step is integrated Retrieval Augmented Generation into the system of the business.

• Select integrated solution suitable: On-premise (on-site): Suitable for businesses that need high data security; Cloud-based (cloud): Suitable for business needs the ability to expand quickly.
• Connect database with the pattern AI: Use the API or framework support (for example: LangChain, Haystack) to connect between the data and model WHO.
• Set the access and content: Specify the check points to ensure information is accurate traceability before model born answers.
• Integrated with the current system: Integrate with CRM systems to chatbot RAG providing customer information accurately. Connection with the management system documentation to employee search internal information easily.

The purpose of this step is the help system of the RAG is connected, works in sync with the existing tools of business.

Step 4: test and evaluate the effectiveness

After integration, it should proceed test thoroughly to ensure RAG operate effectively to meet goals.

• Functional testing: Ensure pattern access and accurate data, provide suitable answers.
• Test performance: Rated speed feedback, the ability to handle as many simultaneous queries.
• Test security: System test to ensure no leaking sensitive data.
• Evaluation results based on KPIs: Measure response rate, accuracy, speed search, and the level of satisfaction of users.

Step 5: optimal scaling

After testing, evaluating success, business conduct optimized scaling, deployment, Retrieval Augmented Generation.

• System optimization: Improving models to enhance the accuracy, speed feedback. At the same time, adjust the database to update with new information constantly.
• Coaching model more data peculiarities: Customize the model to better fit the language needs of the business.
• Scaling applied: Integrated RAG into many different parts, such as parts, customer support, legal department (lookup general legal regulations), The technical department (search assistance, guidance on repair, maintenance),...
• Periodic review: Continuous monitoring, system improvements to meet business needs change.

Thanks to this step, system RAG stable operation, the effect is applied on the interface width in the business.

7. Challenges when applying RAG in business

Deployment Retrieval Augmented Generation brings many advantages for the business but also come with little to no challenge. The identification solve these obstacles is the key to optimize the efficiency of the system RAG.

7.1 Data access to heterogeneous

In the vast majority of business data is often stored in many different formats such as PDF, Word, Excel, or even no data structure from the email and notes. Make the extracted information to train the model Retrieval Augmented Generation (RAG) much difficulty. Data heterogeneity can reduce the accuracy of information traceability leads to results not consistently affect the operational efficiency of the chatbot.

Solution fix: Business needs planning, standardized data before deploying RAG. This process includes:

• Sort, classify data according to each group of functions or departments.
• Data conversion to the unified format such as JSON or XML for easy handling.
• Use OCR technology combined with AI-Data Extraction in order to automatically extract information from the document does not structure.
• Training constantly updated to chatbot properly understand the context, improving the ability to retrieve information.

Solution Server AI of Lac Viet App has the ability to automatically identify, collect information from the text with no structure. Complete control over data, put into ONE, easy trainer WHO fits your specific needs does not depend on the service Tuesday.

7.2 Cost of initial deployment

Applying RAG on chatbot WHO require the cost of the initial investment is quite big for infrastructure, technology, personnel expertise, systems integration. This can be big hurdle for the small and medium enterprises, which have limited budget for the project number conversion.

Solution fix:

• Select implementation partner prestigious as Lac Viet Computingsupply unit LV Chatbot AI Assistant with many services flexibility, including the form of rent, or buy software.
• Integrated gradually phased to allocate the budget reasonable, reduce the financial pressure.
• Leverage the infrastructure availableintegrated with systems such as CRM, ERP to optimize investment costs.
• Support consultant demo free from our expert team to ensure the solution matching the real needs of the business.

Retrieval Augmented Generation not only is an advanced technology that is also 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) brings the groundbreaking solution from the system faq advanced to automate content, support customer service. Not to be left behind, the businesses need to proactively capture, deployment of modern technologies such as RAG to open up opportunities for sustainable development, enhance operational efficiency in the future.

CONTACT INFORMATION:

• Lac Viet Computing Corporation
• 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