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What is the correct way to initialize an LLMChain with an OpenAI model using a prompt template?
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"],
template=prompt_template
)
llm = OpenAI()
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.generate([{"adjective": "funny"}])
```_
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"],
template=prompt_template
)
llm = OpenAI()
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.generate([{"adjective": "funny"}])
```_
Real Exam
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LLeetQuiz
Explanation:
Explanation
Option C is the correct answer because it demonstrates the proper way to initialize an LLMChain with an OpenAI model using a prompt template. Here's a detailed breakdown:
Proper Initialization
- The code correctly initializes the
LLMChainwith both the prompt template and the LLM instance - The
OpenAI()instance is properly created and passed to the LLMChain - This ensures the language model is properly configured for generating responses
Correct Use of Classes and Methods
- PromptTemplate: The
PromptTemplateclass is used correctly with:input_variables=["adjective"]to define the placeholdertemplate=prompt_templateto specify the template structure
- LLMChain: The chain properly combines the prompt template with the LLM instance
- generate() method: The
generate()method is called with the correct parameter format[{"adjective": "funny"}]
Why This Works
- The prompt template defines the structure with
{adjective}as a placeholder - The LLMChain links the template with the OpenAI language model
- When
generate()is called, it fills the placeholder with the provided value and sends the complete prompt to the model - This avoids common errors like incorrect parameter passing or improper initialization
This approach ensures the prompt is properly formatted and processed by the language model for accurate response generation.
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