Recently, news stories and digital content have been painted and many people are believed to feel the same way. There are a variety of new ai terms everywhere, large models, agent, rag, token, and some of the arguments are a bunch of professional words that sound like a cloud。
Although they were trying to use the ai tool, chatting, writing and drawings, many of the concepts were completely out of touch when discussing industry dynamics online. In some cases, when a dry article is painted, a screen full of professional terms is drawn, and it is felt for a long time that a thick wall separates itself from the age of ai。
Many friends felt that these terms were needed by programmers and industry practitioners and that ordinary users could use tools. But that is not the case. Now ai has infiltrated mobile phones, computers, a variety of apps, daily jobs, hiring, starting businesses, selecting digital products, all of which are linked to ai technology. Lack of understanding of the underlying terminology can easily be misled by exaggerated propaganda on the internet, which functions are useful and which are just packaged marketing shrouds。
Today, leaving aside the obscure formula and complex theory, the 12 core ai terms of the current high frequency (hf) appear in plain plain language. You don't need to be a technocrat, or at least paint something that doesn't make any sense。
I. Large language model (llm)
Big language models are the core brain behind most of the ai chat tools now. You can interpret it as a super-knowledge base that has read big open web texts, books and materials. It does not really have the human mind, but learns the big words, learns the human language logic, understands our questions and then responds in a smooth manner。
Our daily bean packs, various types of aai assistants, gemini abroad, etc. Are all based on large language models. It is primarily dedicated to dialogue, writing, summing up information and answering questions, and is the foundation of the generation of the ai era. Many mobile phone novels are promoted at the end of ai, which is essentially a light-quantitative large-language model that can be used locally for simple conversations without networking。
Ii. Token
Token, a lot of people see it, but it's actually the smallest unit for large models of information. The chinese words, words, signs that we entered, ai would not read the whole paragraph, but would cut the text into a small fraction, which would be token。
To give a simple example, in a sentence, “going out to the mall on weekends”, ai will split several token pieces and run them. Token numbers are directly related to two things, the first being the cost of ai services, and the majority of calls to large model interfaces are based on token numbers; and the second is the conversation memory. All the questions you entered, the answers to the ai output, will take up token。
That would also explain why after a long conversation, ai would forget what it had said a long time ago, not that it had deliberately forgotten, but that the token amount had been occupied. When we select the ai tools, look at the parameters, see the context window, the measurement unit is token。
Iii. Context window
The context window can be understood as ai's memory board. This whiteboard has a fixed size, your questions, all the chatting, uploading of the document, all of it on this whiteboard. The larger the whiteboard space, the more the ai can remember。
The context window of the old version of the model is very small, thousands of token, and can only deal with brief conversations; now the new generation of models, many of which are hundreds of thousands or even millions of tokens, you can upload the whole novel, dozens of pages of reports, and get ai to read it all at once。
When ordinary people use it, there is a little practical technique, and if ai begins to forget what has been talked about, without having to dwell on it, to clear some of its historical conversations, which is tantamount to cleaning up the whiteboard, its answers will stabilize again. The purchase of mobile phones, tablet-side ai promotion and context windows are also useful parameters to be noted。
Iv. Purpose (p)I'm sorry
The hint is the complete instruction you gave to ai. Many complain about the empty content of ai's writings, and the answer is not that most of the time it is not a model capability, but rather that the hints are written too broadly。
Just to say, "do me a story," most of the ai output is a panoramic package; if you give all the characters, uses, words, styles, prohibitions to the point, the quality of the output will be much higher. Psychic engineering, which, to be fair, is learning how to give ai a clear and complete job requirement, without making it so obscurantistic that it is best to specify the need。
V. Ai imagy
Ai hallucinations are one of the most common user-plugs. In general, it's an a. I. That makes things up. Its output is consistent and logic looks good, but the time, data, names, events are all fictional。
To be clear, ai was not deliberately lying. The large model predicts the next output on the basis of probability, and when there is no precise answer in its knowledge base, it collides a set of what seems reasonable。
This reminds us that ai gives professional data, news events, parameter information that cannot be used directly, and that important content must be re-checked, especially historical data, product parameters, and that the results of ai outputs must not be reproduced directly。
Vi. Rag search enhancement generation
Rag is the mainstream technology programme specifically designed to mitigate ai hallucinations. For example, the original big model was based on the contents of its own head, and the rag was the equivalent of the opening exam。
We can upload our own files, information, industry documents and create an exclusive knowledge base. When asked, ai will not answer the questions directly by answering the memory, first by searching for the relevant footage in our uploaded database, and then by using the real information that has been retrieved to generate the answers。
The business knowledge base, local documentation and questions, many of them based on rag. However, it must also be seen objectively that the rag cannot put an end to illusions 100 per cent, and that if there is no access to valid information, there is still the possibility of making up content, which cannot be left out altogether。
Vii. Fine-tune
The fine-tuning is a means of directed adaptation of large models. The rag is external information, fine-tuning the equivalent of retraining the model itself, feeding a large number of field-specific samples to make the model more relevant to a particular type of work。
By way of example, the generic mega-model, which is not familiar with autopsies, medical jargons and fine-tuned with a large sample of industry questions and answers to such questions would be more professional and output-styled. The fine-tuning threshold is relatively high, and ordinary individual users rarely operate directly, and more often enterprises, developers, customize their own ai models。
Viii. Agent smarts
Agent, the ai smart body, is also a very hot term for the industry in 2026. Traditional ai is passive, you ask, it answers. Agent's greatest difference is its ability to perform the task of self-dismantling, to plan the steps and to use tools to complete the work package。
Take a live scene and give me the order: check the weekends, if it doesn't rain, screen the surroundings for a short trip, and make a simple list of trips. Agent automatically splits the multi-step task, calls the query tool and completes the whole process step by step, instead of waiting for you to ask one question at a time。
In the future, whether it be a computer or a mobile phone, agent will increasingly be integrated into the system function, helping us to automatically handle a series of complex operations。
Ix. Modalities
The early ai can only read text, a large multi-mode model, and can process information in many forms, including text, pictures, voice and video。
The frequently used uploading of photo questions, interpretation of screenshots, and the direct generation of oral texts are all multimodular capabilities. Mobile phone ais mapping, uploading test papers directly deciphering the subject, screenshots identifying web content and relying on multi-modular technology. When a digital product is selected, the strength and weakness of the polymorphics directly determine whether ai actually works。
X. Aigc-generated artificial intelligence
The full name of aigc is artificial intelligence, which refers to ai's ability to generate content automatically. Ai writes articles, ai paints, ai generates short videos and ai composes, all classified as aig。
It is fundamentally different from traditional ai in the past, and older ai is more selective in identifying, for example, the faces of people in photographs; the core of agc is to create new text, images, audio and video content from zero. There is now a lot of leverage in the media and design industry to improve efficiency, but the issue of content copyright and authenticity still requires more attention。
Xi. Competition
It's easy to understand, it's the ability of ai to calculate, it's the engine in the ai world. Large model training, ai drawings and video generation all require huge computing support。
Why do high-end graphic cards and mobile chips focus on the al-calculations, the higher the calculator, the faster the task is handled. When there is a lack of calculus, ai paints carton, generates slow responses and experience is much less. Whether it's a cloud-sized model server or a mobile phone's local npu chip, one of the core indicators of the puzzle is arithmetic。
Xii. Api interface
Api can imagine it as a universal plugin for the internet world. The large model itself is deployed on a server, with ordinary software, app, without the need to develop ai from zero and to access the full capacity of the large model by calling the api interface。
Many third-party ai applets, software tools do not have self-study models of their own, but call on the api interface, which is open to large plants, to package a shell for general users. It is not necessary for ordinary consumers to look at the bottom logic, but to know that there are many ai applications on the market, the bottom is access to off-the-shelf models through api。
At the end
After these 12 high-frequency terms, it is believed that you can also find that ai is not as high as it would have been. Many of the terms are essentially a description of the different parts of ai's work, which, when broken apart, is perfectly understandable to ordinary people。
We do not have to force ourselves to eat every bottom-up technology, but we know the basic concept, at least to wipe our eyes. Faced with extensive ai marketing campaigns, it is possible to distinguish between what is a solid technological upgrading and what is simply a notion of changing terminology。
The ai era is not about us all becoming technocrats, but about learning to use rational tools to turn technology into an enabler of life and work efficiency, rather than into anxiety caused by strange terms。
So the question is, which of the 12 terms you used to brush before but you never understood? Or what else you can't read, and you're welcome to discuss with the comment section。
