A comprehensive process for building an enterprise RAG system, following the “garbage in, garbage out” principle

0. Enterprise knowledge base governance is important

1. Follow the "garbage in, garbage out" principle. Only by ensuring the high quality of data and processing results at each step of the link can the final effect meet the accuracy. Therefore, enterprise data governance is very important. If data governance is not scientific, there is no need to expect scientific results.

2. RAG is the most recognized data governance framework at present, and we will talk about it today.

1. What is RAG?

Retrieval Augmented Generation, or RAG for short, is an AI framework that combines vector retrieval (Retrieval) and content generation (Generation).

2. Why do we need RAG?

The reason is that the general basic large model can hardly meet our actual business needs. Using the RAG framework to build a private knowledge base for an enterprise is an effective solution to the above problems.

1. There are several reasons as follows:

1) Knowledge limitations: (lack of enterprise private knowledge base)
The knowledge of the model itself comes entirely from its training data, and the training sets of existing large models are basically built on public data on the Internet. Some real-time, non-public or offline data cannot be obtained, so this part of knowledge is impossible to obtain.

2) Hallucination problem: (Without a private knowledge base, hallucinations cannot be suppressed)
The underlying principles of all AI models are based on mathematical probability, and their model output is essentially a series of numerical operations. Large models are no exception, so they sometimes talk nonsense in a serious manner, especially in scenarios where the large model itself does not have certain knowledge or is not good at it. It is difficult to distinguish this kind of hallucination problem because it requires the user to have knowledge in the corresponding field.

3) Data security:
Data security is extremely important to enterprises. No enterprise is willing to take the risk of data leakage and upload its own private domain data to a third-party platform for training. This also leads to the application solutions that completely rely on the capabilities of the general large model to make trade-offs in data security and effectiveness.

3. RAG system composition

The RAG system mainly consists of a knowledge base, a retrieval module and a generation module.

1. The knowledge base stores a large amount of structured and unstructured data of the enterprise, such as internal documents, email records, product manuals, etc.

2. The retrieval module is responsible for matching the user's questions with the information in the knowledge base and finding the most relevant documents.

3. The generation module uses the pre-trained large language model to generate answers based on the retrieved documents.

1. Knowledge base construction:

1) Data integration and cleaning:

Integrate and clean various data sources within the enterprise to ensure data accuracy and consistency.
2) Data labeling and indexing:

Use natural language processing technology to label and index data to improve retrieval efficiency.

3) Data preprocessing module: supports PDF parsing, table extraction, OCR recognition, etc., and uses sliding windows or semantic chunking to optimize text segmentation.

4) Vectorization engine: selects domain-adaptive embedding models, and compresses vector dimensions through quantization technology to reduce storage costs.

 2. Retrieval module:

1) Vectorization retrieval technology:

Convert text into vectors and store them in a vector database to achieve efficient retrieval.
2) Retrieval model selection:

Models such as BERT and DPR are used to accurately match user questions with information in the knowledge base.

3) Reranker: Perform secondary sorting on mixed search results, such as using a cross encoder to improve Top-K relevance.

4) Context enhancement strategy: Use recursive retrieval to solve long-tail queries and refine the search scope through multiple rounds of iterations.

3. Generation module:

1) Pre-trained large language model:

Such as DeepSeek, which is used to generate natural language answers.
2) Fine-tuning and optimization:

Fine-tune the model according to enterprise needs to improve the accuracy and relevance of generated content.
3) Hallucination suppression mechanism: Detect the logical consistency of generated content and context based on rule templates or fine-tuning models (such as LLM-as-Judge).
4) Dynamic parameter adjustment: Adjust the Temperature parameter according to the scenario (such as setting it to 0.8 in the medical field to balance accuracy and creativity).

4. Methodology for Building a RAG System

The construction of enterprise-level RAG system should follow the principle of "phased iteration + data-driven":

1. Demand alignment stage

Clarify enterprise needs: Understand the specific problems that the enterprise hopes the RAG system will solve, such as knowledge management, automated question and answer, etc.

Scenario classification: distinguish between high-frequency core scenarios (such as customer service questions and answers) and long-tail needs (such as cross-document reasoning), and prioritize solving 80% of high-value problems.

Data audit: analyze the type (structured/unstructured), quality (redundancy, consistency) and security level of existing data, and formulate cleaning and labeling rules.

2. Technology selection stage

Design system architecture: including knowledge base, retrieval module, generation module and user interaction interface, etc.

Modular architecture design: adopt decoupled design, for example, separate the retriever from the generator to facilitate independent optimization (such as replacing the vector database or LLM).

Hybrid technology stack: combine open source frameworks (such as LangChain) and commercial components (such as Milvus vector library) to balance cost and performance.

3.

Progressive data access in the engineering implementation phase: first import high-value, low-complexity data (such as product manuals), verify the reliability of the pipeline, and then expand to multimodal content.

Pipeline optimization: for bottlenecks in the retrieval-generation link (such as embedding delays), adopt asynchronous preprocessing and caching strategies (such as FAISS indexing) to improve real-time performance.

4. Continuous monitoring in the evaluation and operation and maintenance phase

: track system performance through indicators such as NDCG and MRR, and establish a knowledge base update SOP (such as weekly incremental indexing).

Fault-tolerant mechanism: design degradation strategies (such as keyword retrieval to cover) to deal with LLM service interruptions or high-load scenarios.
 

V. Practical Application of RAG System
1. Knowledge Management:

Building an internal knowledge base: Helping enterprises organize and manage internal knowledge resources.
2. Employee Training:

Use the RAG system to conduct employee training to improve their professionalism and work efficiency.

3. Automated Q&A:

1) Customer Q&A automation: In the financial industry, the RAG system can help customers quickly obtain detailed information about financial products and improve customer satisfaction.
2) Technical support automation: In the manufacturing industry, the RAG system can build an internal knowledge base to facilitate employees to query technical documents and solutions.
4. Personalized recommendations:

1) Personalized recommendations on e-commerce platforms:

Based on the user's browsing and purchasing history, the RAG system can provide users with personalized product recommendations.
2) Medical diagnosis support:

In medical institutions, the RAG system can assist doctors in making diagnoses and treatment recommendations, thus improving diagnosis and treatment outcomes.

VI. Challenges and Solutions for Building a RAG System

Challenge 1: The model needs to accurately understand the enterprise’s expertise and generate relevant answers.
Solution: Improve model accuracy through supervised learning and fine-tuning training, and cooperate with a manual review mechanism.
System maintenance and update:

Challenge 2: The system needs to regularly update the knowledge base and generate models.
Solution: Establish a regular maintenance and update mechanism to ensure that the system is always in the best condition.