Don’t be naive! If RAG is just about Dify documents, what are engineers doing?

With AI technology changing with each passing day, our expectations for smart assistants have gone far beyond simple questions and answers. Have you ever wondered why some AI answers are accurate and fluent, while others seem to be irrelevant? The answer may lie in RAG (Retrieval-Augmented Generation) - a technology that is quietly reshaping AI capabilities. If you think RAG is as simple as "throwing documents into dify", you may underestimate its complexity. Today, we will not only uncover the core principles of RAG, but also deeply analyze one of the key but often overlooked links - the chunking strategy. After all, if AI wants to answer well, the information fed to it must be cut appropriately first, and this is more particular than you think.

1. What is RAG? How does it work?

Simply put, RAG is like a super smart assistant. It not only answers questions based on its own "knowledge reserve", but also finds the most relevant information from external information bases to assist in answering. Imagine you ask AI: "What are the recent breakthroughs in environmental protection technology?" If AI only answers based on past memories, it may not be comprehensive enough. But with RAG, it will first look through the latest articles and reports to find relevant content, and then combine this information to give an answer.

The specific workflow of RAG can be divided into three steps:

1. Storing information : Convert large amounts of documents (such as articles and reports) into a special mathematical form - vectors, and store them for future use.

2. Matching questions : When you ask a question, AI will turn it into a vector and then find the best matching content in the information database.

3. Generate answers : Finally, AI sends these matched contents and your question to the large language model (LLM) to generate a more accurate and appropriate answer.

Sounds simple, right? But there is a key problem: documents are often very long, and the amount of information that AI can process at one time is limited. What to do? The answer is chunking - cutting large documents into small pieces to make it easier for AI to digest. This step is not only to adapt to the "appetite" of AI, but also directly determines its efficiency in finding information and the quality of its answers.

2. Is RAG really that simple?

When many people hear about RAG, they may think: "Isn't it just deploying a tool, such as Dify, and then throwing documents into it, and AI can automatically answer questions?" This idea is very attractive at first glance, but in actual operation, you will find that things are far from that simple. The implementation of RAG requires not only technical support, but also a lot of adjustments and optimizations based on specific business scenarios.

For example, the quality and format of documents vary greatly. Some may be disorganized notes, while others may be rigorously structured papers. If they are not carefully processed and thrown directly to RAG, AI may not grasp the key points or even give completely off-topic answers. Not to mention that different industries have different requirements for answers: customer service may require concise and clear replies, while scientific researchers may require detailed analysis. The information storage, retrieval accuracy, block strategy and other aspects involved behind this require meticulous polishing at every step. Simply deploying a tool may just be a starting point, not an end point.

3. Why is chunking so important?

Now that we have mentioned chunking, let's talk more about its importance. Chunking is like preparing ingredients for AI. If it is not done well, AI may not be able to grasp the key points or even misunderstand the context. For example, if a complete description of environmental protection technology is cut in half, AI may only see half of it and give a wrong answer. On the contrary, reasonable chunking allows AI to quickly find the most relevant "ingredients" and cook a delicious answer.

The consequences of improper chunking are more than that. If the chunks are too large, AI may not be able to handle them; if the chunks are too small, key information may be fragmented, and the key points may be missed during retrieval. It can be said that the quality of the chunking strategy directly determines the effectiveness of RAG, and this is precisely one of the difficulties that many people underestimate.

Next, let's take a look at RAG's five block strategies to understand their principles, advantages and disadvantages, and the scenarios in which they are suitable.

4. Five Block Segmentation Strategies Revealed

1. Fixed-size chunking: simple and straightforward, but risky

This is the most basic method: split the document into small chunks according to a fixed number of words, terms, or tokens. For example, chunks of 500 words each. To avoid sentences being cut off, some overlap is usually left between adjacent chunks (for example, 100 words).

• advantage :

• The operation is simple and as straightforward as cutting bread.

• Each piece is of the same size, making it easy for AI to process.

• shortcoming :

• Maybe a sentence or a complete thought is cut in half.

• The key information is scattered in different blocks, and it is easy to miss the key points during AI retrieval.

• Suitable scenarios : It can be used when the document content is scattered and the context is not important, but it is not very effective for complex documents.

2. Semantic segmentation: segment by meaning, smart and considerate

This method no longer mechanically divides by word count, but divides according to the "meaning" of the content. The specific steps are:

• First, divide the document into meaningful units such as sentences or paragraphs.

• Generate a vector representation (embedding) for each cell.

• Compare the similarities between adjacent units: if they are very similar, merge them into one; if they are very different, create a new block.

• advantage :

• The natural flow and complete ideas of the content are retained.

• Each piece of content is richer, AI can capture more relevant parts during retrieval, and the answers are more reliable.

• shortcoming :

• You need to set a similarity standard (threshold), and this standard may vary from document to document, so you have to try to adjust it.

• Suitable scenarios : When the document has clear topics or paragraphs, this method allows AI to better understand the content.

3. Recursive block division: layer-by-layer decomposition, flexible and practical

Recursive chunking is a bit like peeling an onion:

• Start by dividing the document into chunks based on natural divisions, such as paragraphs or chapters.

• If a block is too large (exceeds the preset size), it is subdivided until each block fits.

• advantage :

• It preserves the natural structure of the document while controlling the size of the blocks.

• Highly adaptable, suitable for all kinds of documents.

• shortcoming :

• It is a little more complicated than fixed-size chunking and requires slightly more computation.

• Suitable scenarios : This method is very practical when the document has a hierarchical structure and the size needs to be controlled.

4. Chunking based on document structure: follow the "skeleton"

This method directly uses the natural structure of the document, such as dividing it into blocks by title, chapter or paragraph. Each block corresponds to a logical unit, such as a chapter or the content under a subheading.

• advantage :

• Respecting the logical layout of the document makes it easier for AI to understand.

• The block boundaries are clear and management is convenient.

• shortcoming :

• The premise is that the document must have a clear structure. If it is messy, it will not work.

• The block sizes may be uneven, and some blocks may be too large for AI to handle.

• Suitable scenarios : This is very suitable for highly structured files such as academic papers and technical documents.

5. LLM-based block division: Let AI handle it

Since the Large Language Model (LLM) is so smart, why not let it do the chunking? Specifically, give the LLM a task to generate independent and meaningful chunks based on the content.

• advantage :

• The smartest scores have the highest semantic accuracy because LLMs can understand the deeper meaning.

• Each piece of content is of excellent quality and AI is easy to use.

• shortcoming :

• The computation is huge and the cost is high, so it cannot be used casually.

• LLM has limited processing scope and documents that are too long may get stuck.

• Suitable scenarios : When you have sufficient budget and extremely high quality requirements, you can try this "high-end gameplay".

  
  

6. How to choose a block segmentation strategy that suits you?

These five methods have their own advantages and disadvantages. Which one you choose depends on your needs:

• Simple and hassle-free : Fixed-size blocks are quick to use and suitable for scenes with simple content.

• Pursuit of semantics : Semantic chunking and recursive chunking can help AI better understand content and are suitable for documents that require deep understanding.

• Clear structure : The document structure-based chunking is designed for hierarchical documents.

• Not short of money : The LLM-based segmentation effect is outstanding, but it requires sufficient resource support.

In practice, semantic segmentation is often a good starting point, as it strikes a good balance between semantic integrity and efficiency. However, the most reliable way is to try it out according to your document type and goal to find the most suitable one. You may ask: "Can't I just use the tool's default segmentation?" The answer is: Yes, but the effect may be compromised. When it comes to actual business implementation, the optimization of segmentation strategies often requires repeated trials, and even a combination of multiple methods, to achieve the best results.

VII. Conclusion

RAG technology opens the door to a new world for AI's answering ability, and the block strategy is the key to this door. Choose the right "key" and AI will be able to serve you smarter and more accurately. But as we mentioned earlier, RAG is by no means a simple technology that can be "deployed and done". From information storage to block strategy, to retrieval and generation, each step may become a stumbling block to business implementation. I hope this article can help you better understand the essence of RAG and the mystery of block strategy, so that you can avoid detours on the road of exploring AI.

So, next time someone tells you that "RAG is easy, you can just use it", ask them: Have you ever tried to find the precise answer in a messy document? The real challenge is often hidden in the details.