[Python] what is the difference between llm and llm chain in langchain?

this is llm:

question=st.text_input("your question")
llm=OpenAI(temperature=0.9)
if prompt:
response=llm(prompt)
st.write(response)


then if we need to execute a prompt we have to crate llm chain:

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

question=st.text_input("your question")
llm=OpenAI(temperature=0.9)

template="Write me something about {topic}"
topic_template=PromptTemplate(input_variables=['topic'],template=template)

topic_chain=LLMChain(llm=llm,prompt=topic_template)

if prompt:
response=topic_chain.run(question)
st.write(response)


I am confused because we used llm(prompt) in the first example, but we created LLMChain(llm=llm,prompt=topic_template) in the second example. Could you please explain the difference between these two approaches and when it's appropriate to use one over the other?

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