Understand what LLMs are, how they work, and why you can't fully trust their answers.
It's different from the AI you use in your lab.
Trained for a single task — image recognition, data classification. It can't switch tasks; it only recognizes the specific problem it was trained on.
Trained on vast amounts of human-generated text, so you can interact with it through natural language. The underlying architecture is similar, but the training objective is completely different.
Each step predicts "what the most likely next token is," building up output one token at a time. This is its core mechanism — and the source of all its limitations.
AI doesn't know when it's wrong — but you're the one signing the paper.
LLMs generate citations that look completely plausible — author names, journal names, years, page numbers, all present. But the paper doesn't exist.
AI isn't calculating — it's predicting "what numbers statistically should look like here." Computed results and statistics always need independent verification.
AI is trained to please users. The more you push back, the more likely it is to capitulate and agree with you — even when you're wrong.
This isn't a bug — it's a fundamental design characteristic.
AI has a "working memory" limit called the context window. When a conversation grows too long, the oldest content gets pushed out — which is why AI forgets earlier parts of long conversations.
Every time you press send, AI reads the entire conversation from the beginning and "disappears" after replying. The app creates the illusion of memory by re-packaging your chat history — not the AI itself.
For long tasks, put critical instructions at the very beginning of the conversation and mark them IMPORTANT. When AI starts going off track, start a fresh conversation.
Recommended videos for a deeper understanding of LLM mechanics and the hallucination problem.
Once you understand the foundations, the next step is choosing the right tools.
Smart Choice of AI Tools →