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New breakthroughs in AI technology improve answer accuracy! Learn how RAG gives AI an external knowledge base to make answers more reliable.
Core content:
1. RAG technology solves the problem of inaccurate and outdated AI answers
2. RAG working principle: generate answers after retrieving information from an external knowledge base
3. RAG brings advantages such as improved accuracy, real-time information update, and domain knowledge customization
Yang Fangxian
Founder of 53A/Most Valuable Expert of Tencent Cloud (TVP)
Have you ever encountered such a problem: you ask AI questions with great expectations, but it either gives irrelevant answers, as if it is talking nonsense; or the information it gives is already outdated, or even when asked about specific internal affairs or the latest developments of the company, it is completely clueless. This is undoubtedly one of the common challenges currently faced by large language models (LLMs). Today, let's talk in depth about a key technology that can effectively resolve these "awkward moments" - Retrieval-Augmented Generation (RAG).
Simply put, RAG gives the AI plug-in a "super brain" that can be updated in real time, or gives it the ability of "open-book examination", allowing it to check the reliable external information source you specify before answering your question.
The core charm of RAG: not only "smarter", but also "more reliable"
The core idea of RAG is surprisingly intuitive and concise: instead of letting the large model rely solely on the "inventory knowledge" learned during its training, which may be outdated, it is given a new ability - before generating answers, it first retrieves relevant information from a trusted and up-to-date external knowledge base, and then organizes and generates answers based on this fresh "intelligence".
Imagine that you have an extremely smart AI assistant (LLM), but its knowledge base unfortunately stays in the last year. RAG technology is like granting this assistant a vital privilege: before answering your inquiry every time, you can quickly browse the latest materials you specify, such as the latest industry reports, company internal regulations, or even your personal notes. Please note that this "browsing" is not blind guessing, but a precise search based on algorithms.
The benefits of doing so are obvious and significant:
Accuracy is greatly improved: AI's answers are no longer castles in the air or the product of vague memories, but are firmly rooted in retrieved factual evidence. This can significantly reduce the so-called "hallucination" (AI fabricating false information) and make every sentence more well-founded.Real-time information update: External knowledge bases can be dynamically maintained and updated in real time. You can inject the latest news, data analysis, product iteration information, etc. into the knowledge base at any time, and AI will be able to grasp the latest developments immediately, and say goodbye to the somewhat helpless opening line of "Based on my knowledge up to XXXX..."Deep customization of domain knowledge: It is often difficult for general large models to understand the deep terminology of a specific industry or the "jargon" commonly used within your company. Through RAG, you can allow AI to access and efficiently utilize these specialized knowledge bases to create a dedicated AI assistant that truly understands your business needs. For example, you can build an AI that specifically answers fans' questions based on all your past articles and the latest industry research.Answer traceability and transparency: This is crucial in many scenarios. RAG systems can usually clearly show the specific information sources on which the AI's answers are generated. In this way, especially in situations where high accountability is required, you and your audience can trust the answers given by AI more, because you can confidently say: "This AI's answer is not groundless, and the evidence is here."
The secret of RAG's "magic": a complete analysis of the two steps
It sounds like RAG has the magic of turning stones into gold, but if we break down its operation process, it can be clearly divided into two core stages:
Phase 1: Building a knowledge base - carefully creating an "external intelligent brain" for AI
This is like building a professional digital library for AI that is rich in content and available for reference at any time.
Data preparation and selection: First, carefully collect all the data you want AI to refer to, such as internal company documents, product technical manuals, industry research reports, authoritative website content, personal work notes, etc. The key to this link is to ensure the quality and accuracy of the data. After all, high-quality input is the premise of high-value output - as the saying goes, "garbage in, garbage out", which emphasizes the extreme importance of the purity of source data.Chunking : When faced with lengthy documents, it is often difficult to achieve the desired processing effect by directly feeding them to AI. Therefore, it is necessary to use text chunking technology to divide long documents into smaller, more semantically focused text units (Chunks). The advantage of doing so is that AI can more accurately grasp the core meaning of each small paragraph, and it also facilitates the subsequent precise matching of user questions in massive amounts of information.Vector Embedding : Next, we need to use the "Embedding Model" to transform these segmented text blocks into a series of "vectors" composed of numbers. This process can be vividly understood as "translating" the deep semantic meaning of the text into a mathematical language that AI can efficiently understand and compare similarities, thereby giving each information block a unique digital code that can represent its core semantics.Store in Vector Database: The last step is to store these generated vectors and their corresponding original text blocks in a special type of database - vector database. The core expertise of this type of database is that it can quickly retrieve the best matching results for a given query vector from billions of vectors based on semantic similarity.At this point, a structured, well-contented, and always-on-call "professional library" has been built for AI.
Phase 2: Retrieval and Generation — AI’s “Open Book Exam” in Real Time
The smart library is in place, now it's time to put it to use.
Question vectorization: When a user asks a specific question, the RAG system uses the same embedding model as when building the knowledge base to quickly convert the text of the question into a "question vector."Intelligent information retrieval: Then, the system uses the "question vector" as the "key" to efficiently search in the previously constructed vector database, accurately finding several text block vectors and their corresponding original information fragments that are most relevant and most matching to the user's question at the semantic level. This is like an experienced librarian who can quickly locate the most critical pages of reference materials for you in the vast sea of books.Integrate information and generate answers: After successfully retrieving these highly relevant text blocks, the RAG system does not simply throw these raw materials directly to the user. Instead, it submits the user's original question and these selected information fragments retrieved to the large language model (LLM) behind it. LLM will then play the role of an "intelligent integrator". It deeply understands the question and integrates all the input information to finally generate a high-quality answer that not only accurately answers the user's question, but is also fully based on the retrieved facts and expressed in fluent and natural language.Although this process sounds a bit complicated, fortunately, there are many mature open source frameworks and tools (such as LangChain, LlamaIndex, etc.) available, which can significantly simplify the design, construction and subsequent management process of the RAG system.
Keep improving: Tips for further optimizing RAG effects
RAG's performance is already impressive enough, but we, who pursue excellence, can always find ways to take it to the next level:
Hybrid Search: Breaking the limitations of a single search mode, it cleverly combines vector search based on semantic similarity with traditional keyword exact matching search, taking a two-pronged approach to improve the comprehensiveness and accuracy of search results.Fine-tuning Embedding Models: If your application scenario involves highly professional domain terminology or company-specific "jargon", you can consider fine-tuning the selected embedding model. This will enable it to have a deeper understanding of your professional content, thereby generating more accurate and discriminative vector encodings.Re-ranking of search results: After a batch of relevant information is initially retrieved, a key step of "re-ranking" is added before the final answer is generated. By introducing a more sophisticated ranking algorithm, it is ensured that the top candidate information presented to the large language model is the most essential, most relevant, and the highest quality, truly achieving "selecting the best from the best" and ensuring the quality of the answer at every level.RAG vs. Model Fine-tuning: External or Internal Fixes for AI?
When discussing how to make AI smarter, many people may confuse RAG with another common technology - model fine-tuning. What is the difference between the two?
We can simply distinguish them like this:
Model fine-tuning: It focuses more on the "deep cultivation" of the AI model itself. It uses data sets in a specific field to adjust the parameters within the model so that it can learn and master the general knowledge, language style and even the processing logic of specific tasks in that field. This is like sending AI to a special "training class" to further its studies and improve its inherent "internal skills". Fine-tuning is a process that directly acts on and changes the internal structure of the model.RAG (Retrieval-augmented Generation): does not directly modify the core parameters of the large model. It is more like providing an "open reference book" or a "unique weapon list" that can be consulted at any time and updated with new content when AI performs tasks. The model itself does not need (and cannot) store all specific factual knowledge that may change frequently, but relies on temporary access to external knowledge bases when needed.It is worth emphasizing that the two are not mutually exclusive and opposing, but often complement each other. A model that has been carefully fine-tuned and has a deeper understanding of a specific field can often show better performance when executing the RAG process (whether it is information retrieval or answer integration). RAG technology is particularly suitable for complex application scenarios where the knowledge system needs to be updated frequently or the answers are strongly dependent on specific private documents (such as internal corporate knowledge bases).
RAG application scenarios: Let AI truly empower diversified work and life
With its unique technical advantages, RAG has shined in many practical application scenarios, quietly changing the way we interact with information:
Internal intelligent question-and-answer system: If employees want to check the latest company reimbursement policies, the real-time progress of a project, or find complex technical documents, they only need to ask questions directly to the AI system equipped with RAG. AI can quickly locate the most accurate answer from a large number of internal documents and summarize and present it in a clear and easy-to-understand way, thereby greatly improving the efficiency of information acquisition and employee work efficiency.A new generation of intelligent customer service: AI customer service can accurately answer various customer inquiries based on the latest product information and detailed terms of service, effectively reducing errors caused by information lag or misunderstanding, thereby significantly improving customer satisfaction and service experience.Efficient sales support tools: Help sales teams quickly obtain competitive product analysis reports, key points of past success cases, or detailed specifications of specific products, so that they can communicate with customers more confidently and with reason.Assisting decision-making in professional fields: In fields such as healthcare, legal services, and scientific research, which have extremely strict requirements on information accuracy and timeliness, RAG can assist professionals to quickly digest and analyze massive amounts of the latest literature, industry guidelines, or related cases. For example, in the medical field, AI assistants can combine the latest medical research results and clinical guidelines to provide doctors with valuable diagnosis and treatment suggestions, or answer personalized consulting questions based on the patient's specific health status and medical records.The future of RAG: Towards a personalized expert assistant that understands you better
RAG technology is on a fast track of rapid development, and its future potential is exciting:
Intelligent retrieval upgraded again: In the future, RAG will not only be able to find information related to the problem, but also be able to directly locate the small piece of core key content needed to answer the problem from the vast amount of data with amazing accuracy.Embracing the era of multimodal information: RAG’s application boundaries will no longer be limited to pure text processing. Future systems are expected to be able to understand and integrate multiple information modalities such as images, charts, audio and even video to respond to and answer more complex questions that are closer to real-world scenarios.Deep personalized experience: The system will be able to better learn and adapt to each user's questioning habits, knowledge background and preferences, thereby providing more tailored and personalized answers and services.Integration and evolution with technologies such as Long Context: Currently, in addition to RAG, directly providing AI models with ultra-large-scale context windows (Long Context, LC) to allow them to "read" massive amounts of information at one time is also a research direction that has attracted much attention. RAG and LC each have their own advantages and applicable scenarios, and more innovative solutions that cleverly combine the two are likely to appear in the future.As RAG and similar technologies continue to mature and become more popular, the role that AI plays in our lives and work is undergoing a profound transformation. It is no longer just a "generalist" assistant with a broad knowledge base but a "jack of all trades and master of none", but is increasingly evolving into a "personalized expert partner" that truly understands you, your niche, and your company's specific business. Imagine if your AI writing assistant could refer to all your digital notes, the latest industry dynamics analysis, and even your company's internal core knowledge base in real time to provide you with advice and assist in content creation. What an efficient, intelligent, and revolutionary experience that would be?
Summary: RAG, giving AI a credible “source of wisdom”
RAG (Retrieval Augmented Generation) technology, by cleverly connecting AI to an external knowledge base with reliable content and sustainable updates, successfully implements the intelligent, highly reliable answering model of "first search, then generate". This not only significantly improves the accuracy of AI's answers, but also makes the information more fresh and timely, and each answer is well-reasoned, thus effectively solving the inherent "hallucination" problem of large language models and the pain point of lagging knowledge updates.
For everyone who hopes to obtain reliable information from AI, or hopes that AI can better understand and serve specific domain knowledge application scenarios, RAG is undoubtedly a key technology with great strategic value and practical potential. It not only greatly improves the practicality and reliability of AI, but also reshapes our collaboration paradigm with artificial intelligence at a deeper level. A new era of AI that understands us better and understands specific application scenarios is accelerating towards us, and it is worth everyone's continued attention, active exploration and full of expectations!
