answer seems to relate only to the system prompt, not to the actual prompt (user)

I don't understand what I am doing wrong, but it seems answer relates only to the System prompt, ignoring to actual prompt.

Can you help?

def get_completion(prompt: str, system_prompt="", prefill=""):
    response = chat(
        model=MODEL_NAME,
        options=Options(
            max_tokens=2000,
            temperature=0.0,
            num_ctx=8192, # I put context large enough for the txt file.
        ),
        messages=[
            {"role": "system", "content": system_prompt},  
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": prefill}
            
        ],
        #think = True
        #format=ValueList.model_json_schema(),
    )
    return response.message.content

PROMPT = textwrap.dedent(f"""\

Input:
- Read the below <paper> and [prime the focus of the task].
- Pull the most relevant quotes into <scratchpad> tags, as evidences supporting the argument.

<paper>
    {content_doc} # here I inject the text from a large txt file
</paper>


Task:
- Answer the following <questions>, writing the answer for each in an <answer> tag.

<questions>
    <question>
        [..]. # sets of questions
    </question>

""")

SYSTEM_PROMPT = textwrap.dedent(f"""
You are a [Role] who [description of actions should take].
""")

PREFILL = ''

Answer:

I'd be happy to help analyze an argument. Please provide the text of [...].

Once I have the text, I'll proceed with the analysis.

Start by reproducing the get_completion call with the shown system, user, and assistant messages, then inspect the request and returned message content. Verify whether the injected paper text and question reach the Ollama chat API; done means the response uses the user prompt rather than asking for text that was already supplied.