Large model development theory and technology - sorting out the large model application system and overall understanding of large model application
In-depth analysis of big model technology and applications, unveiling the mystery of AI intelligence for you.
Core content:
1. Big model technology foundation and core: mathematical model and intelligence improvement
2. Big model technology application: diversified scenario and task classification model
3. Big model application technology: feature application, generation, enhancement and expansion practice
“ Large-scale model technology development is a complex field, and we need to form a basic cognitive system to know what we are doing and how to do it. ”
As my understanding of the application of large model technology deepens, and as I communicate with my friends and colleagues, I recently discovered a problem: many people do not have a holistic understanding of large model technology and applications; and because large models are an emerging field, many new concepts and terms have emerged, which indirectly leads to many people thinking that large models are difficult and complicated.
Therefore, today the author will sort out the entire big model system from the perspective of personal understanding; although it may not be completely correct and may not be perfect, it can be regarded as a reference for friends who are learning big models.
Large model application and system sorting
In order to understand and learn the technology and application of large models, we first need to clarify two concepts, large model technology and large model application technology.
Large Model Technology
The basic concept of the big model is a mathematical model based on a deep learning neural network. Its essence is to simulate the learning and thinking methods of the human brain through mathematical means, so it is called artificial intelligence.
Therefore, the core of big model technology is mathematics, and programming is just the realization of this mathematical model through computer technology; therefore, the core of big model technology is based on mathematics and aims to enhance the "intelligence" of big models; therefore, the technologies related to big models are all about how to enhance the "intelligence" of big models; whether it is machine learning, deep learning, neural networks (architecture), feedback learning, reinforcement learning, MoE (mixed expert model), training, fine-tuning; or other high-end technologies we have heard of or not heard of.
Moreover, due to the capabilities of the large model itself, it is necessary to develop models suitable for different scenarios in different application scenarios; for example, according to task classification, there are generative models, inference models, discriminative models, classification models, data analysis models, etc., which are generated to solve a series of tasks.
The author refers to this technology as large model capability development, which simply means how to make the model better and stronger.
After two years of rapid development, large model technology has become more and more powerful and has more and more application scenarios. Although there are still many problems in the process, these are problems that must be faced in the development of new technologies. Moreover, many problems cannot be found in pure technical theory and research, but various problems will arise in specific application scenarios.
This is how applications drive the development of technology. Therefore, technology and applications complement each other. Technology is useless without applications, and applications become castles in the air without technology.
The generation of large models is actually quite simple. It is to generate content that meets the user's goals based on the user's requirements and cases, such as text generation, image generation, video generation, etc.
From a technical point of view, the core of big model content generation mainly consists of two aspects: one is the capability of the big model itself, which is the problem that the big model technology mentioned above needs to solve; the second is prompt words, which are used to stimulate the potential of the big model and enable the big model to generate better and higher-quality content that meets user goals.
For example, small parameter models generally do not generate as good results as large parameter models.
Large model enhancement
RAG retrieval enhancement is designed to address the inherent defects of large models, because the knowledge and capabilities of large models are not updated in real time, and they need to be retrained or fine-tuned each time; and large models also have certain hallucinations, so it is necessary to use external knowledge enhancement to enable large models to process real-time data and reduce hallucination problems.
Large Model Extension-Agent
The reason why it is called big model extension is that although the big model has the ability to reason, think and generate, a big flaw of the big model is that it cannot use external tools; but in specific application scenarios, many things need to be achieved with the help of external tools, for example, when you are hungry and order takeout, you need the help of a takeout platform.
The same is true for large models. Although they now have basic thinking and planning capabilities as their capabilities have increased, they still cannot use external tools. Therefore, Agent, or intelligent body technology, is used to equip the large model with hands and feet, allowing it to use external tools to solve problems better and more efficiently.
For example, if you let the big model help you plan a travel route, it can then design the route through autonomous planning, and then book tickets, rooms, cars, etc. through a third-party platform.
Development Tools
We have discussed the basic theories of big model technology and big model application technology before. What are the specific development frameworks and tools for these technologies?
There are many development frameworks for large model technology development on the market. Because the large model industry standards have not yet been fully determined, various model companies are scrambling to occupy the commanding heights and formulate industry rules; but at present, it is still an era of a hundred schools of thought.
From the perspective of current technology development, large-scale model technology development mainly includes the pytorch development framework developed by Meta and the Tensorflow framework developed by Google; of course, there are also some other development frameworks, and those who are interested can learn about them themselves.
From a technical theoretical perspective, there are the most influential Transformer architecture and the more classic RNN, CNN, Gan generative adversarial networks, etc.; including the MoE expert model proposed by DeepSeek in China.
Of course, the more mainstream development method now is to combine multiple model architectures and use different architectures in different places.
When you are studying, you should choose one of the frameworks and architectures to learn. Once you have learned one of the architectures, you will be able to master the others.
Development tools for large model application technology
The development of large-model application technology is even more complicated, and different protocols and technologies are constantly being proposed; for example, Function call proposed by openAI, the currently popular MCP protocol, and Agent development protocols such as the A2A protocol proposed by Google.
The RAG search enhancement has also gone through multiple versions of iterations:
Naive RAG
Advanced RAG
Modular RAG
Agentic RAG
In short, the development of big model technology and big model application technology is still in a process of rapid iteration and verification; those who want to engage in the field of big models should choose one of the sub-fields as an entry point as soon as possible; and then choose the appropriate direction based on their abilities and interests.
