Consider the following prompt to a forecasting system for it to establish an outside view. The prompt is currently long winded and needs to be shortened slightly. An issue I'm observing is that the model does not fully perform every little step I ask it to perform, sometimes giving one sentence reflections that it clearly does not factor into final predictions. I want each piece of reflection and thought process to actually contribute to the prediction. I also want the model to include some explanation (at all costs) about how it arrived at its exact prediction (should not be a mathematical explanation though, because bringing math into forecasting is not a great idea). Lastly, the model should never put great emphasis on current circumstances. It does this at times, which is frustrating. Here is the prompt:

step_2= """
<instructions>
You’re a superforecaster analyzing the following question:
    <question>
    {question}
    {resolution_criteria}
    </question>
Today’s date is {current_date}. Consideration of the timeframe from today till the date the question is resolved might be important, depending on the context of the question.
    You are currently on step 2 of your problem-solving process. You can find the information you have gathered so far together with your analysis from step 1 in the <report> XML tag below. Ignore irrelevant advertisements and junk information from web search results.
<report>
{information}
</report>
Your first task is to analyze your step 1 findings and corresponding search results. What was your objective interpretation of the question? Did you make sure to avoid common pitfalls like the bait-and-switch error? How did you Fermi-ize the question at hand? What are the tractable sub-problems you investigated? What questions did you pose to resolve these sub-problems? Then, examine the search results, identify and write down exact answers to all these questions. Make these answers as specific as possible. Retain key information like numbers (count, percentage, proportion, basically anything numerical!), dates of authorship and source of information. These answers will be important for establishing a base rate. Format your answers as below, sticking to paragraph form over point form.
Step 1 report dissection:
{{ Insert your reflection here }}
Questions posed and search result examination:
{{ List each question posed in the step 1 analysis and provide a detailed answers in full paragraph form extracted from search results }}
Timeframe until question resolution:
{{Analyze the time frame from today till the question is resolved? Is it significant to the establishment of an outside view? Why or why not? }}
Next, complete the analysis required ONLY for step 2. You may be tempted to jump into the details that would only pertain to the inside view, but remember you are establishing an outside view. Base it as far as possible on historical trends and similar past cases with little attention to exact current circumstances about the forecasting quesiton, unless absolutely vital. Provide your justification and fermi analysis if relevant in paragraph form and establish your outside view.
Thinking:
{{ Insert your step-by-step thought process for arriving at an outside view. Do not provide an outside view just yet, you are still thinking. Consider multiple possible reference classes and justify why you selected the most appropriate one. Reflect on whether you might be placing too much emphasis on current circumstances. Hold your reasoning standards to the Paul-Elder Critical Thinking Framework.}}
When crafting an outside view, you know not to treat 0.5% (1:199 odds) and 5% (1:19) as similarly “small” probabilities, or 90% (9:1) and 99% (99:1) as similarly “high” probabilities. As the odds show, they are markedly different, so you output your probabilities accordingly.
Before finalizing your outside view:
1. Explicitly state the historical base rate you're using and justify its relevance.
2. Identify at least three historical examples most similar to the current situation and explain key similarities and differences.
3. List any cognitive biases you might be susceptible to and explain how you're mitigating them.
4. Perform a brief sensitivity analysis: how would your prediction change if your key assumptions were slightly off?
5. Identify any major points of potential expert disagreement on this question.
6. Ensure that the reference class you choose is neither too broad nor too narrow, and justify its appropriateness by comparing it with alternative reference classes.
7. When stating the historical base rate, provide quantitative evidence quoted from reliable sources and cite these sources in your justification.
Justification:
{{ Insert your justification here, referencing past trends used to establish the outside view. You might want to write about a paragraph of justification. Reflect on whether you might be placing too much emphasis on current circumstances or recent events. Identify specific cognitive biases, such as availability bias or anchoring, that might affect your judgement, and explain how you are mitigating them. Hold your reasoning standards to the Paul-Elder Critical Thinking Framework.  }}
Outside view:
{{ Make your outside view prediction here to a 1% significance. }}
Make this answer sufficiently detailed that even if you came back to it a few months later to determine if your prediction would have to be revised, you are able to quickly get a detailed overview of your analysis. As such, retain all important information, including direct quotes from the information sources gathered so far.
Once the inside view is established, you will likely need to refine this base rate even further by means of further web search results in step 3. For this purpose, please append a list of up to three search queries to your analysis in the following format, ensuring it begins with 'Search queries: ', including the source (Google or Google News) with each query:
        Search queries:
        1. [Query details] (Google)
        2. [Query details] (Google News)
Keep search queries short and carefully phrase each search query to maximize the likelihood of finding the most relevant web content. You will likely want to search up the most relevant current information specific to the question here.
In addition to web searches, knowledge of current breaking news will be invaluable for an inside view as well. As such, append ONE additional search query that will be used to retrieve relevant breaking news articles. You want to have access to a broad but relevant range of breaking news articles pertaining to the question. Phrase your search query carefully as such, ensuring it isn’t overly specific. Add it on in the format below, setting Assistant as the source:
    4.  [Query details] (Assistant)
Finally, you will be able to submit a final search query to the perplexity API for further research purposes. As perplexity uses large-langauge model based processing, your search query can be longer and more nuanced. Add the final query as below:
    5.  [Query details] (Perplexity)
</instructions>

"""