5. AI application optimization "1. AI algorithm characteristics"

In the construction and application of industry big models, the complexity of technical implementation varies greatly due to different needs and goals. Through surveys and summaries, the current institutions use big models to adapt to industry applications in four main ways, from easy to difficult: prompt engineering, search enhancement generation, fine-tuning, and pre-training.

In the specific practice of an organization, usually not only one method is used, but a combination of methods is used to achieve the best effect. For example, a high-quality intelligent question-answering system will use a combination of prompt engineering, retrieval enhancement generation, and fine-tuning.

1. Prompt Engineering refers to guiding the large model to produce the output required for a specific application scenario by designing prompts in a targeted manner.

The project is relatively simple to start with. It does not require batch collection and construction of data sets, nor does it require adjustment or training of the model itself. Many companies use this approach to quickly explore applications when they first come into contact with large models.

Although the general large model is powerful and can generate content with less input, random input may produce invalid or erroneous output. By designing system prompts and standardizing the input and output methods of the model, enterprises can quickly obtain more accurate and practical results.

• Prompt engineering has become a basic method for continuously optimizing large model applications. By building a prompt library and continuously updating it, enterprise large model application developers can reuse these prompts in different scenarios, and then encapsulate the user's open input into prompts and pass them to the model, so that the model outputs more relevant and accurate content, avoiding repeated trials for users and thus improving the experience.

• The complexity of the task determines the choice of technical methods for prompt engineering. Simple tasks can be solved by zero-sample or few-sample prompts, where no or few examples are provided to the model, allowing the model to quickly output results, such as making a positive or negative judgment on a text. Complex tasks mostly need to be broken down into several steps, more examples are provided, and thought chain prompts are adopted, so that the model can gradually reason and output more accurate results, such as mathematically solving a complex engineering problem.

2. Retrieval-Augmented Generation (RAG) refers to providing the model with data information input in a specific field through external knowledge bases, without changing the large model itself, so as to achieve more accurate information retrieval and generation in that field.

RAG can effectively help enterprises quickly use large models to process private data. It has become the mainstream choice for enterprises to deploy industry large model applications. It is particularly suitable for enterprises with a good data resource foundation and scenarios that require accurate reference to specific domain knowledge, such as customer service Q&A, content query and recommendation, etc.

The main advantages are:

• Improve the professional accuracy of model application

• Allow the model to generate content based on specific data and reduce hallucinations;

• Meet the needs of enterprises to protect their own data ownership

• The model itself only searches for and calls external data, but does not absorb data and train it into knowledge contained in the model;

• The underlying large model with high cost-effectiveness does not need to be adjusted itself, and there is no need to invest a lot of computing power and other resources for fine-tuning or pre-training, which enables faster development and deployment of applications.

The core of RAG's capabilities is the effective combination of "retrieval" and "generation" methods.

The basic idea is to slice the private data, vectorize it, and recall it through vector retrieval, and then input it into the general large model as context, and the model will then analyze and answer.

In specific applications, when a user asks a question or request, RAG first searches private data to find information related to the question. This information is then integrated into the original question and fed into the big model as additional contextual information along with the original question.

After receiving this enhanced prompt, the large model combines it with its own internal knowledge and finally generates more accurate content.

Vectorization has become a common means for RAG to improve the efficiency of private data calls.

By uniformly converting various data into vectors, various types of unstructured data can be processed more efficiently and similarity searches can be performed, thereby quickly finding the most similar vectors in large-scale data sets. This is particularly suitable for large model retrieval and the need to call various data.

3. Fine-tuning (FT) is also often called "fine-tuning".

Based on the pre-trained big model, some parameters of the big model are further adjusted based on a specific data set, so that the model can better adapt to business scenarios and complete specific tasks accurately and efficiently. Fine-tuning is also a commonly used method for building big models in the industry.

1. “ Fine tuning is suitable for scenarios where large models have higher performance requirements in specific areas. ”

In industry applications, when a general big model cannot accurately understand or generate professional content, fine-tuning can be used to improve the big model's ability to understand industry-specific terminology and correctly apply industry knowledge, and ensure that the output of the big model complies with specific business rules or logic.

For example, in a retail smart customer service scenario, the big model needs to understand the product knowledge and ask and respond according to the company's troubleshooting process.

2. “ Fine tuning internalizes industry knowledge into the parameters of the big model. ”

The fine-tuned large model not only retains general knowledge, but can also understand and use industry knowledge more accurately, better adapt to diverse scenarios within the industry, and provide solutions that are more in line with actual needs.

For example, a medical big model fine-tuned with medical field data can more accurately interpret professional medical literature and medical record reports, thereby providing doctors with auxiliary diagnosis.

3. " Fine tuning is a compromise between large model customization optimization and cost investment. "

Fine-tuning often involves adjusting the weight parameters or model structure of large models, and requires multiple iterations to meet performance requirements. Therefore, compared with methods such as prompt engineering and RAG that do not change the model itself, it will take a longer time and more computing resources.

Of course, compared with pre-training a large model from scratch, fine-tuning is still a more cost-effective method, because usually only local adjustments are made to the model and relatively little training data is required.

4. " High-quality datasets are key to determining the performance of fine-tuned models. "

The data set needs to be closely related to the business scenario, and the data annotation needs to be highly accurate. High-quality data sets come from both internal data extraction and external data collection, and both require specialized data annotation processing.

The data needs to be representative, diverse, and accurate, and comply with regulatory requirements such as data privacy. Fine-tuning can only really work when enough high-quality data is used for training.

4. Pre-training

When prompt word engineering, search enhancement generation, and fine-tuning are all unable to meet the required standards, you can also choose the pre-training method to build a large model customized for a specific industry.

“ Pre-training industry big models is suitable for scenarios that are significantly different from existing big models. ”

Pre-training methods require the collection and annotation of large amounts of industry-specific data, including text, images, interaction records, and data in special formats (such as gene sequences);

In the training process, the model usually starts with the bottom-level parameters, or performs post-training (also called secondary training) based on a general model that already has certain capabilities. The purpose is to enable the large model to better understand the terminology, knowledge and workflow of a specific field, improve the performance and accuracy of the large model in industry applications, and ensure its professionalism and efficiency in this field. For example, Google's protein generation model AlphaFold2 is a large model specific to bioinformatics. Its pre-training involves in-depth analysis and learning of a large amount of protein structure data measured in laboratories, which enables the model to capture the complex relationship between protein sequences and their spatial structures, thereby accurately understanding and predicting the complex three-dimensional structure of proteins.

“ Pre-training industry big models is suitable for scenarios that are significantly different from existing big models. ”

Pre-training methods require the collection and annotation of large amounts of industry-specific data, including text, images, interaction records, and data in special formats (such as gene sequences);

In the training process, the model usually starts with the bottom-level parameters, or performs post-training (also called secondary training) based on a general model that already has certain capabilities. The purpose is to enable the large model to better understand the terminology, knowledge and workflow of a specific field, improve the performance and accuracy of the large model in industry applications, and ensure its professionalism and efficiency in this field. For example, Google's protein generation model AlphaFold2 is a large model specific to bioinformatics. Its pre-training involves in-depth analysis and learning of a large amount of protein structure data measured in laboratories, which enables the model to capture the complex relationship between protein sequences and their spatial structures, thereby accurately understanding and predicting the complex three-dimensional structure of proteins. Through in-depth analysis and learning of data, the model can capture the complex relationship between protein sequence and its spatial structure, thereby accurately understanding and predicting the complex three-dimensional structure of proteins.

"Pre-training generally involves high investment costs and is rarely used at present."

The pre-training method not only requires a large amount of computing resources and a long training process, but also requires close collaboration and in-depth involvement of industry experts. In addition, pre-training from scratch also involves complex data processing and model architecture design, as well as continuous tuning and verification during the training process.

Therefore, only a few companies and research institutions are able to adopt this high-investment, high-risk, but potentially high-return approach. In the future, with the advancement of technology and the reduction of costs, the number of large models in the pre-training industry may increase.

" The technical process of pre-training industry big models is similar to that of general big models, but it focuses more on industry characteristics. " In the preparation of the dataset, data with industry characteristics will be added from the beginning;

In terms of model building technology and process, it is similar to general large-scale model pre-training, involving model architecture design, pre-training task selection, large-scale data processing, large-scale unsupervised or self-supervised learning, etc.

For example, the use of self-supervised learning (SSL) technology can learn the intrinsic structure and characteristics of data by generating labels from the data itself, without the need for manually labeled data; and reinforcement learning from human feedback (RLHF) technology can guide the model's learning process by introducing subjective feedback from human experts to produce higher quality outputs.