Building an enterprise-level AI model-driven application system is a comprehensive task that crosses the boundaries of technology and business, which not only tests the depth of knowledge in the business domain of enterprises, but also challenges the technical height of enterprises to build applications based on AI large models. This process requires close collaboration between business experts and AI model experts to ensure that the multiplier effect of business value is realized through the empowerment of AI models.
Based on practical experience, the construction process of AI large model application can be systematically divided into five core steps:
1) Precise definition of the demand scenario
2) Scientific selection of large models
3) Enhanced tuning of large model performance
4) Deployment, operation and maintenance of large models, and
5) Seamless integration of AI applications.
For each key step in this process, this article will provide an in-depth analysis of the strategic choices and potential challenges faced by enterprises, aiming to provide a actionable action guide for enterprises that intend to ride the wave of AI model development and accelerate digital and intelligent transformation.
1. Clarify the demand scenario: precise guidance to ensure that the target is clear
Defining the requirements scenario is the starting point of the project and the cornerstone of the project's success. AI model experts need to work closely with the business team to carefully analyze the business pain points, identify the key problems that AI can solve, and consider regulatory compliance and resource constraints to set clear and realistic goals for the project.
- Vague demand targets can lead to wasted resources and project delays. Enterprises should first clarify the scenarios of AI applications, such as whether they need to handle specific tasks such as text generation, sentiment analysis, image understanding, and generation, which directly affect the subsequent model selection and technical route design.
- Failure to adequately assess the potential risks in advance. For example, in China, large models and large model applications that provide services to foreign countries need to go through security evaluation and filing. This requires careful consideration when selecting models to avoid risks such as the use of overseas large models and data transmission to avoid compliance issues.
After clarifying the demand scenarios and goals in this stage, it is of guiding significance for the subsequent guidance model selection, evaluation of computing resources and budgets, design reasonable technical solutions, identification of security compliance requirements, and even control the deployment and operation and maintenance of large model application routes. It is also a link that enterprises themselves need to focus on.
In general, it is recommended to start from the point to the surface, starting with the combination of single-point AI large model capabilities and existing applications, gradually considering the combination of more in-depth and more scenarios, and finally reshaping business applications based on the idea of AI agent.
2. Large model selection: balance the art and grasp it accurately
In the large model selection stage, AI large model experts select many pre-trained models based on the requirements analysis results, considering not only the performance and accuracy of the model, but also the balance of computing efficiency, cost, and security, and selecting the solution with the best compatibility with enterprise infrastructure.
- Performance vs. cost trade-off: This requires a detailed look at the various large models available on the market. Benchmark to understand model caps and make decisions based on actual budget and performance needs, while considering the long-term maintenance costs of your model. Generally speaking, we will have such a large model to choose a triangle, which is balanced from the three aspects of effect, performance and cost. In the selection path, it is recommended to first use the "smartest" basic large model (such as the 100 billion parameter scale of the general Qianwen MAX version, and the multi-modal version is the qwen_VL_max version) to do the upper limit effect test and verification of the task, if the "smartest" large model can meet the effect requirements, and then consider the cost and performance issues, such as down-selecting a small parameter scale large model and then testing, until the balance meets the comprehensive needs of the enterprise.
- Security and compliance considerations: It is particularly important in China to choose a large model that meets the requirements when it comes to the safety and reliability of the content generated by the large model, the filing of the large model and its application, and the restrictions on data export.
- AI toolchain and ecosystem support are essential for the continuous optimization and function expansion of large models. A strong AI toolchain and community support mean more use cases, tools, and solutions, which help enterprises quickly iterate and upgrade. Huggingface is the most famous community in foreign countries, and Alibaba Cloud's Modelscope community in China.
3. Enhance and optimize the large model: meticulously crafted and improve efficiency
In the subsequent enhancement and tuning steps, the selected large model is optimized through prompt word engineering, RAG, fine-tuning, and other solution strategies, aiming to improve its performance and reliability in specific scenarios and ensure that the output of the large model meets the actual needs of the business.
- Prompt word engineering refers to the design of various inputs to guide the behavior of the model to give a reply. It is characterized by light weight, easy to use, and strong model relevance. Although the prompt word project is light and easy, it also needs to understand the characteristics of the large model in order to design high-quality prompt words and efficiently guide the model to output the expected content.
- RAG mainly combines external knowledge data to enable large models to answer vertical/closed domain questions more controllably. RAG relies on high-quality external data, so the accuracy and timeliness of the data, and the optimization skills in the process of data retrieval and enhancement are the key, and poor handling will affect the output quality of the large model.
- Fine-tuning is the process of small-scale training to optimize the performance of a model on a specific task. It is characterized by high accuracy and adaptability to specific tasks, but is complex and costly. At the same time, there is a certain uncertainty in the fine-tuning process, and improper handling will lead to overfitting and even affecting the original basic model ability.
For the current three mainstream optimization schemes, here is a brief supplement. People often ask what is the core difference between the above three solutions and how to choose? Let's start with the question of what. As shown in the figure below, in essence, large models, like programs, are based on external inputs, and then give output results after execution. Program = data structure + algorithm, simple correspondence, large model = model structure + parameter weight. Therefore, the essence of prompt word engineering is to better adapt the input to the model structure in the large model through clever design input, so as to obtain better output results. Fine-tuning is based on the given dataset for training, to optimize and update the parameter weights that have been fixed during the pre-training of the large model, so that the subsequent inputs can get better output results on the tasks in the industry.
Let's move on to the question of "how to choose". In the actual use of the large model, the enhancement and tuning of the large model is not carried out according to the linear path of prompt word engineering->RAG-> fine-tuning and tuning. Instead, it is necessary to combine the characteristics of prompt word engineering, RAG, and fine-tuning, and try again and again, and spirally push to the ground. The following figure shows a suggested path for large model tuning given by OpenAI experts.
After fine-tuning and tuning, it is important to do a good job of model evaluation. The model evaluation not only verifies the optimization effect of the model on specific tasks and ensures that the output quality meets the expected standards, but also provides data support for further iterative optimization by comprehensively evaluating the performance of the model and revealing potential biases or deficiencies. I will not repeat the specific evaluation, but you can refer to the relevant articles on the Internet to understand.
In the process of enhancing and tuning large models, datasets are very important, whether it is a training dataset or an evaluation dataset. This is because high-quality data (including the diversity, quality, and scale of data) profoundly affects the capabilities of large models and the value of enterprise AI applications. Many enterprises often ask how to improve the application effect of large models when we lack data or do not have high-quality data. In this case, in addition to suggesting that enterprises can try a variety of data cooperation methods, they can also directly use the base model or ready-made industry-specific large models, and at the same time start to plan and build their own enterprise data platform.
4. Large-scale model deployment and operation: flexible and adaptable to ensure stability
Once the model is optimized and mature, it will enter the deployment and operation phase, which requires experts to carefully design the deployment architecture, whether it is cloud hosting, edge computing, or on-premise deployment, to ensure the stable operation, elastic scaling, and efficient operation and maintenance of the system, and establish a monitoring mechanism to deal with potential failures. The deployment method of large models should be determined based on the business scenario objectives of the enterprise. Depending on the size of the model parameters, it can be deployed to terminal smart devices, IDC data centers, and the cloud. Cloud platforms generally provide more large-scale model environment deployment and operation services according to different scenarios. This is shown in the figure below.
- It is recommended that most enterprises give priority to directly calling MaaS API services (similar to Alibaba Cloud Bailian Model Service Platform).
- If an enterprise needs to deploy an open source large model privately, it should first consider the overall development and deployment based on Alibaba Cloud's artificial intelligence platform PAI to provide model services. If you have an AI team and technology accumulation, and you are considering building your own, you can recommend building it based on Alibaba Cloud GPU ECSs. In terms of cost and O&M investment, it is not recommended to build self-built intelligent computing resources and large model services in an offline IDC in principle.
5. AI application integration: Deep integration to unleash potential
Whether it is through MaaS API interfaces, plug-ins, process orchestration, Agent, or building a new user interface, the goal is to maximize the capabilities and value of large models, improve user experience, and promote the intelligent upgrading of enterprise business processes, thereby driving a leap in enterprise innovation and competitiveness.
The author of this article: Shaojun chattering Source: Shaojun's AI space
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