I too have tried to fine-tune the large model right from the start, until I realized how wrong I was!
In-depth analysis of the two magic weapons for improving the accuracy of large language models: RAG and fine-tuning.
Core content:
1. RAG: equip AI with a real-time search engine to quickly obtain the latest answers
2. Fine-tuning: let AI "memorize" knowledge directly and blurt out professional answers
3. Comparative analysis of the applicable scenarios, advantages and disadvantages of RAG and fine-tuning
Suppose you are preparing for a final exam in college. You have studied the textbooks for a whole semester and have memorized the core knowledge. You don't even need to look up the book for the exam. But suddenly, someone asks you a new question that is not covered in the textbook. You are a little confused and quickly grab your phone to check Baidu. After finding the answer, you answer confidently. These two scenarios correspond to the two "magic tools" we use to improve the accuracy of large language models (LLMs):
Retrieval-augmented generation (RAG) — allows AI to query external knowledge bases at any time to obtain the latest answers.
Fine-tuning - allowing AI to "remember" knowledge directly through additional training.
Whether it is ChatGPT, claude , or DeepSeek, the existing large language models (LLMs) are powerful, but the knowledge is "dead". Once it exceeds the training range, it will become unreliable. So, how to make them smarter and more practical? Today, we will use the most down-to-earth way to analyze the core principles and key differences between RAG and fine-tuning, as well as how to make the best choice in different scenarios. After reading this article, you will find that making AI smarter is actually not complicated!
1. RAG vs. Fine-tuning: Who is your “AI cram school”?
1. RAG: Install a “real-time search engine” for AI
RAG, the full name of which is Retrieval Enhanced Generation, simply put, gives your AI assistant an "e-book" that can be flipped through at any time. When you ask a question, it will first "look up information" in the knowledge base, and then combine its own language ability to give you a reliable answer.
How does it work?
You ask: "What is the new tax policy this year?"
AI turns problems into “digital codes” (vectorization) for easy searching.
Extract relevant information from external knowledge bases (such as company documents and web pages).
Finally, an answer that is both informative and natural is generated.
example:
Enterprise customer service : If you ask “How many days do I have left for my annual leave?”, ordinary AI may be at a loss, but RAG will check the HR system in seconds and tell you: “There are only 5 days left!”
Legal advice : Need the latest regulations? RAG searches in real time to ensure your answers are fresh.
Medical scenario : When a doctor asks about the treatment for a new virus, RAG can instantly find the latest research.
advantage:
Ultra-flexible : As soon as the knowledge base is updated, the AI can “learn” new things without having to be retrained.
Wide range of scenarios : RAG thrives in the rapidly changing fields of finance, healthcare, and law.
Save money : No need to make major changes to the model, and low deployment cost.
shortcoming:
A little slower : After all, you have to "flip through the book", which is not as fast as answering directly.
Relying on data : If the knowledge base is wrong, AI will also fail.
2. Fine-tuning: Let AI “memorize” knowledge directly
Fine-tuning is another way - without looking up information, let AI "carve" knowledge into the brain directly. Just like you memorize the knowledge points by doing exercises and memorizing them, so that you can speak them out fluently during the exam.
How does it work?
Prepare professional data (such as legal documents, medical reports).
Use this data to "teach" AI and adjust its "brain circuits".
After training, AI can directly output professional answers.
example:
Legal assistant : After fine-tuning, AI can spit out legal advice directly without flipping through books.
Medical AI : After feeding it a bunch of medical data, it can accurately analyze the condition and prescribe a diagnosis and treatment plan.
Company Assistant : After fine-tuning internal information, AI can answer company policies and procedures in seconds.
advantage:
Super fast : No need to look up information, just get the answer directly from your brain.
Super stable : In professional scenarios, the answers are accurate and reliable.
Specialization : Suitable for fixed tasks, such as industry-specific AI.
shortcoming:
Updating is troublesome : when new knowledge comes, you have to "review" it again.
High cost : It requires a large amount of data and computing power, which is both costly and brain-intensive.
2. One picture to understand: the difference between RAG and fine-tuning
In fact, RAG and fine-tuning are not mutually exclusive. Many awesome companies have adopted a "combination punch": RAG allows for flexible data search, while fine-tuning ensures professionalism and accuracy. This is particularly popular in large vertical industry models .
How to choose? Ask yourself these questions:
Does knowledge change quickly?
Yes → Use RAG (e.g. news, policy advice).
No → Use fine-tuning (e.g. legal text, medical diagnosis).
Need to be super professional?
Yes → Fine-tuning (such as financial risk control and manufacturing quality inspection).
No → RAG (e.g. customer service chat, general Q&A).
On a tight budget?
Try RAG first, and then make fine adjustments if the effect is good.
Want speed or flexibility?
Fast → Fine Tuning.
Flexible → RAG.
4. How to choose the best solution?
In actual projects, more and more companies choose to combine RAG and fine-tuning , especially in vertical domain large models (Vertical Domain LLM). RAG provides flexible knowledge retrieval, while fine-tuning ensures the accuracy of industry-specific tasks.
Does the knowledge change frequently? Select RAG.
Does the AI’s answer need to be highly specialized? Choose fine-tuning.
On a budget? Start with RAG and fine-tune when necessary.
Do you want a faster answer or a more accurate answer? Choose fine-tuning for faster results and RAG for more flexibility.
By combining RAGs and fine-tuning appropriately, your AI assistant can not only acquire industry expertise, but also update its knowledge at any time, making it truly your intelligent work partner!
