What can AI do? What can’t it do for now? Try to fill the information gap of ordinary people
Unveil the mystery of AI large language models and discover their potential and limitations.
Core content:
1. Basic introduction and application background of large language models (LLM)
2. Practical functions of LLM in writing, translation and consulting
3. How to use LLM correctly to improve work efficiency and quality of life
Key Takeaways:
Large Language Models (LLMs) are a type of AI that excels at everyday tasks such as writing, translating, and consulting. It is efficient, but it cannot truly understand emotions or replace professionals for the time being. Understanding its capabilities and limitations, and mastering the correct way to use it, will allow you to use it better.
Introduction: Why ordinary people need to know about LLM
In recent years, artificial intelligence (AI) has developed rapidly, and large language models (LLM) have gradually come into the public eye. Whether it is DeepSeek or Doubao, these tools have attracted many users from non-tech industries to try them out. However, many people were disappointed after their first experience, thinking that AI was "useless" or "inaccurate in answering questions."
This misunderstanding often stems from a lack of understanding of the nature of AI, especially a lack of understanding of the capabilities and limitations of large language models (LLMs). LLMs are AI tools trained on large amounts of text data that can generate and understand human language and are widely used in writing, consulting, and translation. Understanding its capabilities and limitations can help us make better use of it and improve our lives and work efficiency.
This article will introduce in detail what LLM (such as Deepseek, Doubao, etc.) can do and what it cannot do for the time being, and use examples to illustrate how to use it rationally and eliminate the misunderstanding that "AI is useless."
1. Definition and Background of LLM
LLM (Large Language Model) is an AI technology that focuses on processing and generating human language. It learns from massive amounts of text on the Internet, books, and social media to master the grammar, semantics, and usage patterns of language.
Simply put, it is like a super smart "text generator" that can predict and generate appropriate answers or content based on your input. Common examples include DeepSeek, Doubao, Tencent Yuanbao, etc. These tools have been integrated into the daily lives of many people, such as writing, translation or consulting.
For ordinary people, the appeal of LLM is that it can be used without professional technical background. Whether it is a teacher writing a lesson plan, a business writing an advertising copy, or a traveler needing translation, LLM can provide help.
2. What can LLM do: practical functions and examples
The practicality of LLM is reflected in the following aspects, each of which is closely related to the lives of ordinary people:
Writing Assistant
Function: LLM can help you generate various text contents, such as job application letters, reports, advertising copy or birthday wishes. Example: If you are a small business owner and want to write a product description, LLM can quickly generate attractive text, such as "Our handmade soap is natural and harmless and will protect your skin." Applicable scenarios: Students use it to write essay outlines, and professionals use it to polish emails, saving time and improving efficiency. Answer questions and provide advice
Function: LLM can answer various questions and provide practical suggestions, which is equivalent to a 24-hour online knowledge assistant. Example: You ask, "How do I fix a leaky faucet?" It might answer, "First turn off the water valve, remove the faucet, check if the seal is damaged, and replace it if necessary." Applicable scenarios: When planning a trip, ask it "Where to go this weekend?" It can recommend attractions and activities; in daily life, ask it how to make fried rice with egg, and it can give you the steps. Language Translation
Function: LLM can translate one language into another and is suitable for cross-language communication. Example: When traveling, you can use it to translate menus. For example, if you input "I want to order beef noodles" in Chinese, it will output "The beef noodle, please." in English. Suitable for: International business email translation, communicating with locals while traveling, or even practicing conversation when learning a foreign language. Intelligent dialogue
Functionality: LLM can carry on natural conversations, tell jokes, chat with others, and even handle customer service issues. Example: If you say "Tell a joke", it may answer: "Why did the astronaut break up with his girlfriend? Because he needed space!" In the customer service scenario, ask "When will my order arrive?" It can query and reply. Applicable scenarios: Chat with it when you feel lonely; e-commerce platforms use it to answer common questions and reduce the pressure on manual customer service. Can't really understand emotions
Problem: LLM can simulate empathy, but it doesn't actually feel the emotion. It just generates responses based on patterns, not really caring about how you feel. Example: If you say "I'm in a bad mood today", it may reply "I'll hug you, don't be sad", but this is just preset comfort, which is different from the sincere support of friends. Risks: If you need emotional support, LLMs are no substitute for human companionship and may leave you feeling empty. Lack of real creativity
Problem: LLM can generate content that imitates existing styles, but cannot invent something completely new. Its "creativity" is based on training data and lacks originality. Example: If you ask it to write a poem in the style of Li Bai, it may be good, but it will not create a whole new literary genre. Risk: In fields that require innovation, such as art and design or scientific research, the LLM is of limited use. May be biased or wrong
Problem: The output of an LLM depends on the training data, and if the data is biased or erroneous, it may reflect those issues, leading to unfair or inaccurate responses. Example: If you ask “What kind of people are programmers?” it might say “They are all men and love to drink coffee”, but this is obviously not comprehensive and may reinforce stereotypes. Risk: On sensitive topics, it may mislead users and the information should be verified carefully. No substitute for professionals
Question: LLM can provide general information, but it cannot replace professionals such as doctors, lawyers or counselors. Example: You ask “What should I do if I have a headache?” It may suggest “Drink more water and rest”, but it cannot diagnose a specific disease and you need to see a doctor. Risks: Over-reliance can lead to misjudgments, especially in the medical or legal fields, with potentially serious consequences. Vague requirement: "Write an environmental article." Result: The LLM may produce a generic, unfocused article that touches on a variety of topics, such as climate change and pollution, but lacks depth. Specific requirements: "Write a 500-word article focusing on the impact of plastic pollution on marine ecology and proposing at least three solutions." Result: LLM can generate more targeted and clearly structured articles with focused content that meets your expectations. Use clear instructions: Tell the LLM what you need specifically, e.g. “write an email to thank the client” is clearer than “write an email”. Provide context: If the task has background information, provide relevant details, such as "Summarize three key points from last week's meeting." Specify the output format: If you want it presented as a list, table, or a specific word count, specify that in advance, such as “summarize in three paragraphs” or “list five recommendations.” Actively try LLM tools , especially in writing, information search and daily consultation, it can significantly save time. Pay attention to the quality of requirements . The more specific and clear the requirements are, the better the output of LLM will be. Look at its limitations rationally . For emotional support or professional advice, it is more reliable to seek human experts.
These features show that LLM is not a distant technology, but a tool that can directly improve our lives.
III. What LLM cannot do for now: Limitations and risks
Although the Large Language Model (LLM) has demonstrated amazing capabilities, being able to process massive amounts of information and complete a variety of tasks, it is by no means omnipotent. Clearly recognizing its shortcomings can not only help us not to rely too much on it and remain rational during use, but also prevent us from easily judging it as "useless" before we have a deep understanding of it, thus missing out on its true value.
Here are a few key “can’ts”:
These limitations remind us that LLM is a tool, not a person. It can help us complete tasks, but it cannot replace human ability in emotion, creativity, and professional judgment.
4. Let LLM complete the task better: skills of making requirements
Whether LLM can complete the task well depends not only on its ability, but also on how you ask questions (i.e. "prompt"). Asking questions is the way users interact with LLM. Good questions can help LLM understand your intentions more accurately and generate higher quality output. On the contrary, unclear or unspecific questions may cause LLM to generate irrelevant or wrong answers.
Example
Skills in making demands
With these tips, you can better guide LLM and get more satisfactory results. Asking for requirements is like giving LLM a clear "task book". The more specific, the better the LLM's performance.
For more tips, you can follow this article: Mastering AI Prompt: A Guide to Writing Effective Prompts with Large Language Models (LLMs)
5. Comparison: LLM’s capabilities and limitations at a glance
For a more intuitive understanding, here is a comparison table of LLM capabilities and limitations:
| category | What can be done | Can't do anything for now |
|---|---|---|
| Writing and creation | ||
| Information and advice | ||
| Language Processing | ||
| Dialogue and Interaction | ||
| Accuracy and Fairness |
This table helps us clearly see that LLM is strong in everyday tasks but still has gaps in complex or sensitive areas.
VI. Conclusion and Suggestions
LLM is a powerful AI tool that performs well in writing, translation, consulting, and chatting, significantly improving the efficiency of work and life. However, it also has obvious limitations: it cannot truly understand emotions and lacks original creativity; at the same time, due to possible bias in training data, its output results also need to be carefully verified. Most importantly, it cannot currently replace the roles of professionals such as doctors and lawyers.
Suggestions:
Understanding these will allow you to use AI more rationally, enjoy its convenience, and avoid over-dependence. AI is a tool, not a person. It can help you complete tasks, but it cannot replace human emotions and creativity.
