Answers.Body: <p>ChatGPT is running a Python program to compute the SHA-256 hash value. If you click on [&gt;_], you'll see the code it used.</p>
  <p>In my case, it is using the following program:</p>
  <pre><code>import hashlib
  
  # Text to hash
  text = &quot;ABCD&quot;
  
  # Calculate SHA-256 hash
  hash_value = hashlib.sha256(text.encode()).hexdigest()
  hash_value
  </code></pre>
  
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Answers.Text: Whoever downvoted cares to explain why?
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Answers.Body: <p>you need to learn how chatgpt prompt engineering works learn here is it one the best courses to learn chatgpt prompt engineering <a href="https://www.udemy.com/course/master-chatgpt-prompt-engineering/" rel="nofollow noreferrer">https://www.udemy.com/course/master-chatgpt-prompt-engineering/</a></p>
  
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Answers.UserDisplayName: Ajwad Hassan
Body: <p>I read the paper <a href="https://arxiv.org/abs/1706.03762" rel="nofollow noreferrer">Attention Is All You Need</a>, and understood the architecture. Searching the Internet, there is a consensus that ChatGPT is (<em>just</em>) a super-sophisticated auto-complete engine, thanks to Large Language Model (LLM). That said, it doesn't truly understand questions like humans [2], [3].</p>
  <p>When it <em>seems</em> to not know a thing because the thing was <strong>not</strong> in the training data, it will <em>try</em> to spit out something anyway [1], leading to so-called hallucination.</p>
  <p>I tested it with a term that I'm sure never appeared on the Internet ever. And it managed to answer correctly. In the example below, I believe that the 1st term &quot;ABCDEF&quot; probably existed somewhere in the Internet, e.g. in Computer Science 101 class note/lecture; but the 2nd term is my randomly typed text.</p>
  <p>                 <a href="https://i.sstatic.net/XIMINyKcl.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/XIMINyKcl.jpg" alt="enter image description here" /></a></p>
  <ul>
  <li><p>How could ChatGPT &quot;match/generate&quot; the answer for the 2nd example <strong>correctly</strong>? It can't just match the most probable document somewhere.</p>
  </li>
  <li><p>Can I safely assume that before feeding into LLM engine, ChatGPT service parses the question first, to see if it is a <strong>low-hanging fruit question</strong>, e.g. &quot;what is today?&quot;, &quot;what's the weather like today?&quot;, &quot;when is the flight XYZ from A to B?&quot;. Exactly like Google/Bing is already doing. If so, the parser diverts the question (or part of the question) to another specifically designed engine, e.g. HashCalculatorEngine?</p>
  </li>
  </ul>
  <h3>References</h3>
  <p>[1] <a href="https://ai.stackexchange.com/questions/38270/how-does-an-ai-like-chatgpt-answer-a-question-in-a-subject-which-it-may-not-know">How does an AI like ChatGPT answer a question in a subject which it may not know?</a></p>
  <p>[2] <a href="https://www.reddit.com/r/computerscience/comments/ze21bp/how_does_openais_chatgpt_seem_to_understand_things/" rel="nofollow noreferrer">How does OPENAIs ChatGPT seem to “understand” things</a></p>
  <p>[3] <a href="https://news.ycombinator.com/item?id=34123186" rel="nofollow noreferrer">https://news.ycombinator.com/item?id=34123186</a></p>
  
Tags: <large-language-models><chatgpt><semantics>
Title: How can ChatGPT correctly answer questions containing things that it surely never saw?
UserDisplayName: JoyfulPanda
ThreadText: # How can ChatGPT correctly answer questions containing things that it surely never saw?
  
  (Asked by: username_0 on 2024-10-05)
  
  I read the paper [Attention Is All You Need](https://arxiv.org/abs/1706.03762), and understood the architecture. Searching the Internet, there is a consensus that ChatGPT is (*just*) a super-sophisticated auto-complete engine, thanks to Large Language Model (LLM). That said, it doesn't truly understand questions like humans [2], [3].
  
  When it *seems* to not know a thing because the thing was **not** in the training data, it will *try* to spit out something anyway [1], leading to so-called hallucination.
  
  I tested it with a term that I'm sure never appeared on the Internet ever. And it managed to answer correctly. In the example below, I believe that the 1st term "ABCDEF" probably existed somewhere in the Internet, e.g. in Computer Science 101 class note/lecture; but the 2nd term is my randomly typed text.
  
  [![enter image description here](https://i.sstatic.net/XIMINyKcl.jpg)](https://i.sstatic.net/XIMINyKcl.jpg)
  
  * How could ChatGPT "match/generate" the answer for the 2nd example **correctly**? It can't just match the most probable document somewhere.
  * Can I safely assume that before feeding into LLM engine, ChatGPT service parses the question first, to see if it is a **low-hanging fruit question**, e.g. "what is today?", "what's the weather like today?", "when is the flight XYZ from A to B?". Exactly like Google/Bing is already doing. If so, the parser diverts the question (or part of the question) to another specifically designed engine, e.g. HashCalculatorEngine?
  
  ### References
  
  [1] [How does an AI like ChatGPT answer a question in a subject which it may not know?](https://ai.stackexchange.com/questions/38270/how-does-an-ai-like-chatgpt-answer-a-question-in-a-subject-which-it-may-not-know)
  
  [2] [How does OPENAIs ChatGPT seem to “understand” things](https://www.reddit.com/r/computerscience/comments/ze21bp/how_does_openais_chatgpt_seem_to_understand_things/)
  
  [3] <https://news.ycombinator.com/item?id=34123186>
  
  **Tags:** large-language-models, chatgpt, semantics
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2024-10-06. Score: -1
  
  ChatGPT is running a Python program to compute the SHA-256 hash value. If you click on [>_], you'll see the code it used.
  In my case, it is using the following program:
  import hashlib
  
  # Text to hash
  text = "ABCD"
  
  # Calculate SHA-256 hash
  hash_value = hashlib.sha256(text.encode()).hexdigest()
  hash_value
  
  > **Comments:**
  > **username_1**: Whoever downvoted cares to explain why?  
  
  ---
  
  ### Answer by username_2 on 2024-10-25. Score: -1
  
  you need to learn how chatgpt prompt engineering works learn here is it one the best courses to learn chatgpt prompt engineering <https://www.udemy.com/course/master-chatgpt-prompt-engineering/>
  
  > **Comments:**
  > **username_3**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  