Prompting has a significant effect on the quality of LLM responses. Prompts can be further customized to improve the overall quality of the responses from the LLMs.
For example, in the given template, the following prompt was used to generate a response from the LLM, where question is the user query and context are the documents retrieved by the retriever.
custom_prompt_template = """Use the following pieces of context to answer the question at the end. If the answer to the question cannot be extracted from given CONTEXT than say I do not have information regarding this.
{context}
Question: {question}
Helpful Answer:"""
CUSTOMPROMPT = PromptTemplate(
template=custom_prompt_template, input_variables=["context", "question"]
)This modification can be done in the following location:
file: app.py
function: get_conversation_chain
For further examples, we encourage you to visit any of the following resources: