Correct llm pitfall in pitfalls.md (#1086)

This looks very much like a Freudian slip, as the abbreviation LLM was probably translated by GPT as “Language Learning Model”.

docs/basics/pitfalls.md

@@ -15,7 +15,7 @@ import Pitfalls from '@site/docs/assets/basics/pitfalls.svg';
 - Understand the biases and problems that LLMs have
 :::
 
-Language Learning Models (LLMs) are powerful tools that have revolutionized many aspects of technology, from customer service to content creation. However, like any technology, they are not without their flaws. Understanding these pitfalls is crucial for effectively using LLMs and mitigating potential issues. This article will explore some of the common pitfalls of LLMs, including issues with citing sources, bias, hallucinations, math, and prompt hacking.
+Large Language Models (LLMs) are powerful tools that have revolutionized many aspects of technology, from customer service to content creation. However, like any technology, they are not without their flaws. Understanding these pitfalls is crucial for effectively using LLMs and mitigating potential issues. This article will explore some of the common pitfalls of LLMs, including issues with citing sources, bias, hallucinations, math, and prompt hacking.
 
 ## Citing Sources
 
@@ -47,4 +47,4 @@ LLMs can be manipulated or "hacked" by users to generate specific content. This
 
 ## Conclusion
 
-In conclusion, while LLMs are powerful and versatile tools, they come with a set of pitfalls that users need to be aware of. Issues with accurately citing sources, inherent biases, generating false information, difficulties with math, and susceptibility to prompt hacking are all challenges that need to be addressed when using these models. By understanding these limitations, we can use LLMs more effectively and responsibly, and work towards improving these models in the future.
+In conclusion, while LLMs are powerful and versatile tools, they come with a set of pitfalls that users need to be aware of. Issues with accurately citing sources, inherent biases, generating false information, difficulties with math, and susceptibility to prompt hacking are all challenges that need to be addressed when using these models. By understanding these limitations, we can use LLMs more effectively and responsibly, and work towards improving these models in the future.