Under the wave of intelligent customer service, the application of AI big models is gradually becoming an important tool for enterprises to improve service quality and operational efficiency. However, unoptimized AI big models often cannot accurately understand user needs, and may even cause customer dissatisfaction due to inappropriate responses. For example, a bank's customer service team reported that when a user consulted about ETC invoice issues late at night, the AI ​​big model actually replied "It is recommended that you consult during daytime working hours", which led to customer complaints. The emergence of this problem shows that AI big models must be fine-tuned in detail to truly adapt to the business needs and user expectations of enterprises.

This article will explore how to efficiently fine-tune the DeepSeek model to ensure that it has stronger business understanding, more accurate answering capabilities, and a more natural interactive experience. Through systematic fine-tuning, companies can not only improve customer service response speed, but also reduce the transfer rate to manual work, significantly improving customer satisfaction.

1. The core difference between fine-tuning and knowledge base

In the process of optimizing large AI models, it is important to clearly distinguish between fine-tuning  and knowledge base (RAG, Retrieval-Augmented Generation)  . Although the two have similarities, their functions are very different.

1. Fine-tuning: giving AI big models deep learning capabilities The essence of fine-tuning is to conduct targeted training on AI big models so that they can understand the company's unique business logic, service language, and customer communication style. Unfine-tuned AI big models usually can only rely on general language models to answer, and it is difficult to accurately match the company's specific business scenarios. For example, if an insurance company's AI big model is not fine-tuned, it may not be able to correctly understand the professional term "cash value", but after fine-tuning, it can accurately identify and give a reasonable explanation.

2. Knowledge base: providing real-time information support The role of the knowledge base is similar to the "external brain" of the AI ​​model. It allows AI to retrieve FAQs, policy documents, product manuals and other content in real time when answering questions. The introduction of RAG technology enables the AI ​​model to find the most matching information in the internal data pool of the enterprise like a search engine and integrate it into answers. However, the knowledge base can only provide factual information and cannot improve the logical reasoning ability and interactive performance of the AI ​​model.

3. The synergy between fine-tuning and knowledge base If the AI ​​model relies solely on the knowledge base, it may still give irrelevant answers or generate answers that do not conform to the corporate style. Therefore, enterprises should adopt a two-pronged strategy of "fine-tuning + knowledge base". Fine-tuning can improve the business understanding ability of the AI ​​model, making it more language-logical and industry-adaptable; the knowledge base can ensure that the answers of the AI ​​model are always based on the latest corporate data. The combination of the two can greatly improve the customer service ability of the AI ​​model, enabling it to understand complex scenarios and provide accurate information.

Why is fine-tuning so important?

The importance of fine-tuning for large AI models is not only reflected in optimizing the quality of their responses, but also in significantly improving the overall efficiency of customer service operations and user experience satisfaction. Through fine-tuning, companies can transform general AI models into intelligent tools that better meet their business needs, thereby solving pain points in actual scenarios.

For example, an e-commerce company achieved remarkable results after fine-tuning its large AI model, which not only verified the value of fine-tuning, but also provided experience for other companies to learn from. The process of fine-tuning is essentially to enable AI to better understand the company's language environment, service logic and user needs. This targeted optimization makes AI perform better in complex scenarios, while reducing the need for manual intervention, saving costs for the company and improving service quality.

Decreased transfer rate to human operators:  In the un-fine-tuned AI model, due to its limited ability to understand complex problems, it is often necessary to frequently transfer conversations to human customer service, with a transfer rate as high as 38%. This not only increases the workload of human customer service, but also prolongs the waiting time of users and reduces service efficiency. After fine-tuning, the AI ​​model can more accurately identify user intentions and give reasonable responses, and the transfer rate to human operators has dropped significantly to 12%. This change means that AI can handle more problems independently, reducing the pressure on human customer service, while also allowing users to get solutions faster and improving the overall service experience.

Improved response speed:  The fine-tuned AI model has significantly improved its efficiency in understanding customer questions and organizing answers, with an average response time of 1.7 seconds shorter. Although this number may seem small, the cumulative effect is very significant in customer service scenarios with high-frequency interactions. The reduction in customer waiting time directly improves their user experience, especially in industries such as e-commerce and banking that require extremely high response speeds. Fine-tuning optimizes AI's language processing and logical reasoning capabilities, enabling it to quickly grasp the core of the problem and give clear answers, thereby creating a smoother communication experience for users.

Improved user satisfaction:  The fine-tuned AI model can generate answers that are more in line with user needs, which directly promotes the improvement of customer satisfaction. According to statistics, after fine-tuning, the user satisfaction of an e-commerce company increased by 25%. This improvement is due to the fact that AI learned expressions that are more in line with the company's business logic and user expectations during the fine-tuning process, such as more accurate answers, more natural tones, and more personalized solutions. The improvement in satisfaction not only enhances user loyalty, but also wins a reputation advantage for the company in market competition, fully demonstrating the far-reaching impact of fine-tuning in improving service quality.

Fine-tuning of large AI models is particularly critical in the following scenarios, because these scenarios place higher demands on the accuracy and adaptability of AI:

Enterprises have their own industry terminology. Many industries have unique terms and expressions. These terms often carry specific business meanings, and it is difficult for large AI models that have not been fine-tuned to accurately understand and use them. For example, in the financial industry, "credit score" is not just a simple word, but requires a comprehensive interpretation based on complex data such as the user's loan record and repayment history; in the medical industry, the subtle differences between "preliminary diagnosis" and "formal diagnosis" may directly affect the patient's subsequent treatment decisions. If AI cannot correctly understand the meaning of these terms, it may give incorrect answers or even mislead users. Through fine-tuning, AI can learn industry-specific language systems, thereby providing more accurate support in professional scenarios.

Enterprises have special service processes. The service processes of some enterprises are highly complex and unique. For example, bank identity verification requires multiple authentication steps, and the airline's rebooking policy may involve multiple conditions such as fare adjustments and time limits. If the AI ​​large model has not been fine-tuned in a targeted manner, it may not be able to accurately understand the logic of these processes, resulting in an inability to effectively guide customers to complete operations, and even causing user dissatisfaction. For example, an un-fine-tuned AI may only provide general information when a user asks about rebooking, but cannot provide detailed steps based on the specific policies of the airline. The fine-tuned AI can improve service efficiency and user experience by learning the company's service rules and providing guidance that complies with the process.

Companies have specific customer service scripts. The customer service communication styles of different companies vary significantly, and this style is often closely related to the brand image and target user groups. For example, e-commerce customer service usually uses a friendly and lively language style to narrow the distance with consumers; while legal consulting services need to use formal and rigorous expressions to reflect professionalism and authority. If the language style of the AI ​​large model does not match the needs of the company, it may make users feel uncomfortable or even cause misunderstandings. Through fine-tuning, AI can adjust its language style according to the company's brand positioning and user preferences, such as learning to use Internet terms that are closer to young users, or maintaining a formal tone in high-end services, so that communication is more natural and in line with expectations.

3. Standard process for fine-tuning large AI models

1. Data preparation - building high-quality training data Data preparation is the basic link for fine-tuning large AI models, and its quality directly determines whether the effect of fine-tuning can meet the needs of the enterprise. High-quality data can not only help AI learn business logic accurately, but also avoid answer errors caused by data bias. Therefore, at this stage, enterprises need to invest enough energy to collect, organize and optimize training data to ensure its authenticity, representativeness and compliance. Only through scientific data preparation can AI truly understand the business scenarios of enterprises and perform well in practical applications.

Collect real customer service conversation records:  In order for AI to learn real conversation patterns, companies need to extract high-quality data from historical customer service records. These records should cover a variety of user questions and customer service responses, and sensitive information (such as name and phone number) needs to be anonymized to ensure compliance with data privacy regulations. Real conversation data can help AI master language habits and problem-solving methods in actual scenarios, thereby improving its applicability in customer service scenarios.

Organize frequently asked questions and standard answers:  Based on historical data and business needs, companies need to extract frequently asked questions and design standardized best answers for each question. These answers must not only be accurate, but also meet the company's service standards and brand tone. For example, for the common question "return process", the standard answer should clearly list the steps and provide contact information to ensure that users can operate smoothly.

Covering a variety of expressions:  Users ask questions in a variety of ways, so training data needs to include formal expressions, colloquial conversations, and even regional dialects or slang. For example, "I want to return the product" and "Can you give me a refund?" express the same need, but the tone and wording are completely different. Through diverse data input, AI can improve its language adaptability and better cope with the communication habits of different users.

⚠ Avoid using the following data for training:

• • Data containing customer privacy information: Using un-massified data may violate privacy regulations and bring compliance risks.

• • Unresolved complaint cases: These cases may contain incorrect responses, which, if learned by AI, may cause it to repeat the bad responses.

• • Randomly fabricated sample data: Fictional data may be out of touch with real business scenarios, affecting the practicality of AI.

2. Choose the right fine-tuning tool

When fine-tuning large AI models, enterprises need to choose appropriate tools based on their own technical capabilities, budgets, and business needs. Different tools have their own advantages in ease of use, efficiency, and flexibility. Reasonable selection can significantly improve the effect and implementation efficiency of fine-tuning. The following are several common options and their applicable scenarios:

• • DeepSeek Official API: For small and medium-sized enterprises with limited technical capabilities, this tool provides plug-and-play fine-tuning capabilities. Enterprises can quickly complete fine-tuning by simply uploading data and setting basic parameters without having to delve into complex algorithm details. This lowers the technical threshold and enables more companies to easily enjoy the benefits of AI fine-tuning.

• • Unsloth: For enterprises that want to speed up the fine-tuning process, Unsloth is an efficient choice. By optimizing the training algorithm, it can complete high-quality fine-tuning in a shorter time, which is particularly suitable for scenarios with tight time constraints or frequently changing business needs. Its advantage is that it takes into account both speed and effect, which is very suitable for medium-sized enterprises.

• • Self-built training platform: For large enterprises with strong technical strength, self-built platform provides the greatest flexibility. Enterprises can customize fine-tuning process, adjust model architecture or optimize parameters according to specific needs. However, this method requires a lot of manpower and computing resources, and the cost is high. It is suitable for companies that have extremely high requirements for AI performance and have the ability to invest in the long term.

3. Optimization of key parameters

During the fine-tuning process, parameter adjustments directly affect the learning effect and performance of the AI ​​large model. Enterprises need to optimize key parameters based on actual conditions to ensure that AI can quickly adapt to new data while avoiding deviations or overfitting in learning.

• • Learning rate: This is the core parameter that controls the learning speed of AI. If the learning rate is set too high, AI may pay too much attention to the noise in the new data, resulting in learning the wrong pattern; if it is too low, the training process will become slow and even fail to fully grasp business knowledge. A reasonable learning rate needs to be adjusted continuously in the experiment to find the best balance.

• • Batch Size: The batch size determines the amount of data processed during each training, which directly affects the training efficiency and the use of hardware resources. If the batch size is too large, it may exceed the video memory capacity, resulting in training interruption; if it is too small, it may slow down the model convergence speed and affect the final effect. Enterprises need to choose an appropriate batch size based on hardware configuration and data scale.

• • Number of training epochs: The number of training epochs determines the number of times the AI ​​learns from the data. If there are too many epochs, the AI ​​may overfit the training data and lack the ability to generalize to new questions; if there are not enough epochs, it may lead to insufficient learning and poor answer quality. Through multiple tests, companies can find the number of training epochs that best suits their needs.

4. Comprehensive testing after training is completed

Before the AI ​​model goes online, comprehensive testing is an indispensable step. Through testing, enterprises can evaluate the accuracy of AI's answers, logical coherence, and scenario adaptability to ensure its stable performance in actual applications. Testing needs to cover a variety of situations to verify the comprehensive performance of AI.

• • Routine question testing: Check whether AI can accurately answer the company’s frequently asked questions, such as “how do I check the order status” or “how long does a refund take?” This step mainly verifies the basic capabilities of AI.
• • Complex question testing: For questions involving multiple steps or multiple conditions, such as “how to transfer money after the account is frozen”, test whether the AI ​​can correctly understand and give a complete answer.
• • Unexpected question testing: Simulate users asking unexpected or vague questions, such as "What's wrong with this thing?", observe the AI's response ability, and ensure that it does not generate inappropriate or off-topic answers.

The optimization of AI big models is not achieved overnight, but requires enterprises to regard it as a continuous iterative process. As the business environment changes, user needs change, and industry trends evolve, AI big models must keep up with the times by regularly updating training data. Only by constantly inputting the latest conversation records, business rules, and user feedback can AI always have the ability to solve practical problems. In addition, a scientific fine-tuning strategy is the core of this process, which requires companies to be rigorous and professional in data preparation, tool selection, parameter adjustment, and test verification. Through such efforts, companies can create an AI customer service big model that is accurate, efficient, and highly in line with their needs.

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