prompt-engineering-research

Prompt Engineering for Research

A skill for applying systematic prompt engineering techniques in academic research contexts. Covers prompt design patterns, evaluation methodologies, and practical workflows for using large language models (LLMs) as research tools.

Prompt Design Patterns

Core Prompting Strategies

Research-Specific Prompt Templates

def create_research_prompt(task_type: str, context: dict) -> str:
    """
    Generate a structured prompt for common research tasks.

    Args:
        task_type: One of 'literature_summary', 'methodology_critique',
                   'code_review', 'data_interpretation', 'writing_feedback'
        context: Dict with task-specific context
    """
    templates = {
        'literature_summary': """
You are an academic researcher specializing in {domain}.

Summarize the following paper excerpt, focusing on:
1. The research question and its significance
2. The methodology used
3. Key findings and their implications
4. Limitations acknowledged by the authors
5. How this work relates to {related_topic}

Paper excerpt:
{text}

Provide a structured summary in 200-300 words. Distinguish clearly
between what the authors claim and what the evidence supports.
""",
        'methodology_critique': """
You are a methods expert reviewing a research design.

Evaluate the following methodology description:
{text}

Assess the following:
1. Internal validity: Are there confounding variables not controlled?
2. External validity: How generalizable are the findings?
3. Statistical approach: Is the analysis appropriate for the data?
4. Sample: Is the sample size adequate? Any selection bias?
5. Reproducibility: Could another researcher replicate this?

For each concern, rate severity (minor/moderate/major) and suggest
a specific improvement.
""",
        'data_interpretation': """
You are a statistical consultant helping interpret results.

Given these results:
{results}

Context: {context_description}

Provide:
1. Plain-language interpretation of each result
2. Effect size interpretation (is it practically significant?)
3. Potential alternative explanations
4. Caveats the authors should mention
5. Suggested follow-up analyses

Be precise about what the data does and does not support.
Do not overstate findings.
"""
    }

    template = templates.get(task_type, templates['literature_summary'])
    return template.format(**context)