ChatGPT, Gemini, Deepseek and other generative AI popularization today, AI tools are changing the way we work. But the same tools, some people can quickly get accurate answers, some people are stuck in the "invalid question" predicament.
The reason for this is often not a lack of AI capability, but that you have ignored the most basic law of communication - the GIGO principle (garbage in, garbage out). After analyzing a number of mainstream AI tools in real testing, I found that the following five misconceptions are quietly eroding your questioning efficiency.
1. Over-generalization of needs
Don't learn from the TV series that "I want to see the program before going to work tomorrow", AI doesn't have the comprehension of workplace veterans. It doesn't have the situational reasoning ability of human beings, and when the demand lacks specific parameters, the system can only randomly combine them based on a huge amount of data.
For example, if you ask for a marketing strategy without specifying the type of product, target group, and budget size, the generated program will often be generalized. An effective approach is to build a "5W1H" framework: specify the demand scenario (Where), the target audience (Who), the core objective (What), the timeframe (When), the key constraints (Why/How), and build the problem framework with details.
2. Lack of clarity on formatting requirements
When the AI is asked to "analyze the competition", you may get a prose narrative, but what you actually need is a comparison table that can be put directly into the PPT. The output format is not only a formal requirement, but also determines the organizational logic of the information.
Dump the template directly when you ask a question, "Compare the camera parameters of Huawei P70 and Apple 15 in a three-column table, with the name of the feature in the first column, the specific value in the second column, and the keywords of the user reviews in the third column".
Remember, AI is a typographic toolman with no aesthetic, and it has to be taught by hand how to present information.
3. Non-zeroing of chat records
Chatting with AI is afraid that it "remembers too well", the last time I asked it to imitate Lu Xun's style to write a paragraph, and this time I asked about the terms of the contract, and it still carries the same accent as before.
It's like cooking congee directly in a pan that's been fried in a spicy hot pot; it always has a lingering peppery flavor. Remember to knock two lines before an important conversation, "Forget all previous conversations, now start the discussion from zero...". This kind of active cutting can avoid 78% the relevance of the error, really uneasy, just open a new dialog.
4. Passive acceptance of wrong answers
classifier for objects with a handleAI ConversationTreating it as a one-time query for search engines is a typical cognitive misunderstanding. When the first response is off, Ultra 60% users choose to re-ask the question rather than iteratively optimize it.
In fact, the use of "diagnostic communication" is very effective: first point out that "the third point of the data has a deviation of 151 TP3T from the authoritative report", then provide the correct parameters, and finally ask for a "recalculation based on the corrected data". ". This two-way debugging mechanism can increase the accuracy of the second answer to 921 TP3T.
5. Neglecting the boundaries of instrumental capabilities
Trying to use chat AI to generate professional design drawings, or having a drafting model write rigorous legal documents, is essentially a tool mismatch. Each AI has a clear area of specialization. General-purpose conversational models are good for information integration, programming-specialized types excel at code generation, and data analytics plug-ins are adept at numerical processing.
Experienced users create "capability matrices" that document the thresholds of strengths of different tools. When a problem takes more than 15 minutes with no progress, it is wise to switch to a specialized tool or return to manual processing, rather than stubbornly "teaching the AI to do what it can't do".
at last
Artificial Intelligence is reshaping the way knowledge is acquired, but the more advanced the technology, the more important it is for users to master the art of "accurate communication". When we learn to talk with machine logic, we can truly break through the constraints of GIGO's law and make every question a productive one.
Well, that's all I have to share today, if you find it useful you are welcome to bookmark and share, thanks!