Prompt Engineering Guide for Scientists

This Guide teaches you how to design clear, structured prompts for modern AI models—using context, roles, examples, and best practices—so you can reliably get accurate, usable results for scientific research, writing, and analysis.
(Updated: May 26, 2025)

Introduction: What Is Prompt Engineering and Why Does It Matter?

Prompt engineering is the skill of designing instructions for large language models (LLMs) so you get answers that are accurate, useful, and reliable.
This is essential with advanced models—GPT-4.1/4o (OpenAI), Claude 4 (Anthropic), Gemini 1.5 Pro (Google), LLaMA-3, and Mixtral 8x7B—that handle text, images, or audio, with built-in tools and memory.

For scientists, a “prompt” is more than a question: it’s your way to tell the AI exactly what you want—be it a literature summary, data analysis, code, or peer review.
Good prompt engineering lets you:

• Save time and avoid repetitive work
• Get consistent, verifiable results
• Minimize mistakes and AI hallucinations
• Make the most of features like citations, tool-calling, and memory

This guide will help you:

• Write clear, reproducible prompts for major LLMs
• Use techniques like few-shot, RAG, chain-of-thought, tool-calling
• Get structured output (lists, tables, JSON, XML, etc.)
• Work safely and responsibly with AI
• Understand model/provider differences
• Apply real research examples, plus a glossary

Quick Glossary

• • LLM: Large Language Model (AI that understands and generates human language)
  • Prompt: The instruction or question you give the AI
  • Context window: How much text/data the AI can “see” at once (bigger is better)
  • Few-shot prompting: Showing the AI examples before your question
  • RAG: Retrieval-Augmented Generation (add up-to-date info in your prompt)
  • Chain-of-thought (CoT): Telling the AI to “think step by step”
  • Tool-calling: The AI can trigger tools (search, code, fetch data, etc.)
  • JSON/XML: Structured data formats so the AI returns information in a clear structure
  • Self-critique: The model checks/reviews its own answer for you
  • Temperature: Controls how creative or consistent the model’s responses are

Fundamentals: Writing Clear and Effective Prompts

The Four-Stage Prompt Stack (2025 Standard)

LLMs work best when prompts are organized by roles, just like people in a workflow:

<System>     // Who/what is the AI? (e.g., "You are a medical reviewer…")
<Developer>  // Technical rules/format constraints (e.g., "Always output as JSON")
<User>       // Your instruction or question
<Critique>   // (Optional) Tell the model to double-check or self-critique

Example:

<System> You are a scientific literature expert.
<Developer> Output in JSON format as shown below.
<User> Summarize the following article in three sentences and list any limitations.
<Critique> Review your answer for missing information.

Context First, Then the Question

Always put your background info (abstract, table, dataset) before the question. Use clear separators/headings or XML tags so there’s no confusion.

Example:

<context>
  <abstract>Paste the article abstract here.</abstract>
</context>
<user_task>
  <instruction>Summarize the main findings in 3 bullet points and list 2 key references.</instruction>
</user_task>

Be Explicit About Output Structure: JSON and XML

JSON (“JavaScript Object Notation”) is a simple, widely-used way to organize data as key-value pairs. Think of it like a labeled list or simple spreadsheet.

{
  "summary": "A short answer here",
  "references": ["Smith et al. 2023", "Lee et al. 2024"]
}

XML (“eXtensible Markup Language”) organizes data with tags (like <summary>…</summary>). Common in science publishing and for more complex structures.

<response>
  <summary>A short answer here</summary>
  <references>
    <ref>Smith et al. 2023</ref>
    <ref>Lee et al. 2024</ref>
  </references>
</response>

Why use JSON or XML? They keep results neat, consistent, and easy to use in Excel, databases, or apps.
Tip: If you’re not familiar, just ask for “a list of bullet points” instead, or copy/paste one of the format examples above into your prompt.

Role-Playing for Clarity, Style, and Perspective

Role-playing tells the AI who to act as (a persona/expert). This is powerful for adjusting detail, tone, and style.

• Expert detail: “You are a clinical trial statistician.”
• Science communication: “You are a science journalist writing for lay readers.”
• Policy focus: “Act as an environmental policy advisor.”

How to use:

• Start your prompt with the role. Ex: “You are an expert in environmental chemistry. Explain the findings in simple language.”
• For two styles: “Explain the results as a specialist, then as you would to a high school student.”

Benefits: The right level for your audience; less jargon or missed points.

Tip: Try several role-plays and compare outputs!

Positive Guidance and Ambiguity Avoidance

Don’t just say what not to do (like “No opinions!”). Give positive, concrete instructions:
“Report only the observed results and their significance. If data is missing, write ‘insufficient data’. Do not speculate.”

Iterative Prompting & Verification

1. Start simple—see what the model does
2. Review for errors/confusion
3. Refine: Add context, clarify, or specify output format
4. Use a <Critique> role for AI self-check
5. Repeat as needed

Always double-check AI outputs, especially in research!

Few-Shot Prompting and In-Context Learning

Give the model 1–3 examples of your task before your real question. “Show, then ask.”

Example 1: "The new compound increased yield by 15%." Outcome: Positive
Example 2: "No significant difference was observed." Outcome: Negative
Now classify: "The catalyst doubled the reaction rate." Outcome:

Try zero-shot first (no examples). If the answer is off, add examples. Always put the real question at the end (“Now” or “##” as separator).

Retrieval-Augmented Generation (RAG)

For up-to-date science, add the info you want analyzed as a <context> block before your instruction.

<context>
  <doc1>Microplastic levels in the Pacific Ocean are rising... (Smith, 2023)</doc1>
  <doc2>2024 field studies report... (Lee, 2024)</doc2>
</context>
<user_task>
  <instruction>Summarize key findings about microplastic trends in the Pacific Ocean and cite the sources.</instruction>
</user_task>

Tip: If the answer is wrong, check your context block for missing info or if it’s too long for the model’s limit.

Chain-of-Thought (CoT) Prompting for Complex Reasoning

CoT means making the AI “show its work” step by step.
Zero-shot: Add “Let’s think step by step.”
Few-shot: Give a reasoning example, then your real question.

Q: If a DNA sequence is 20% adenine, what % cytosine?
A: 20% adenine means 20% thymine. That leaves 60% for guanine + cytosine. Since they’re equal, cytosine is 30%.
Now answer: [your question]

Self-consistency: Try several runs; pick the answer given most often (“majority vote”).

Tool-Calling and Memory (Advanced Use)

Tool-calling: The AI can trigger a function (search for a DOI, fetch a gene, etc.)

{
  "function": "get_paper_by_doi",
  "parameters": { "doi": "10.1234/example.doi" }
}

Memory: Some AIs can store facts or context for your session (e.g. MEMORY: key=value).

Simulating API-Like Features in Web Apps (Prompt-Level Workarounds)

Some advanced AI features—like structured outputs, self-checking, or calling external tools—are usually available when using a programming interface called an API (Application Programming Interface). But even if you’re using a web app like ChatGPT or Claude without programming, you can still get similar results using smart phrasing in your prompts.

Here’s how to mimic common API behaviors through your prompt wording:

• Structured output (e.g. JSON):
  API version: A developer can request response_format = "json".
  Prompt workaround: “Respond only in valid JSON like: { ’summary‘: …, ‚references‘: […] }”
  This helps if you want to copy results into Excel, R, or Python.
• Using tools (e.g. calculator or code):
  API version: The AI can run code or fetch data.
  Prompt workaround: “You are a calculator. Return only the numeric result of the following expression.”
  Or: “Write Python code to calculate the statistical power. Do not explain the code.”
• Self-checking (model reviews itself):
  API version: Some systems can run an automatic second review.
  Prompt workaround: “(1) Answer the question. (2) Now double-check your response and improve it if needed.”
  This can improve accuracy and reduce hallucination.
• Controlling creativity:
  API version: Developers set temperature for more or less randomness.
  Prompt workaround: Say “Be precise and factual—avoid creative phrasing” or “Give 3 creative alternatives.”
  Repeat prompts or use “Regenerate” to sample more outputs.

These prompt-level tricks let you use AI more reliably—even without programming or API access. If you find results inconsistent or too vague, try refining your instructions using examples like these.

Features of Web-Based AI Tools (ChatGPT, Claude, Gemini, Mistral)

Most AI platforms today—like ChatGPT, Claude, Gemini, and Mistral—offer built-in tools in their web interfaces that go beyond text generation. These features let you upload files, generate plots, access real-time web data, or remember user preferences between chats. Here’s what each system currently supports (as of mid-2025):

ChatGPT (OpenAI)

• File uploads: You can attach files such as PDFs, spreadsheets, or images. The model can read and analyze their contents.
• Code interpreter („Advanced Data Analysis“): ChatGPT can run Python code for data analysis, statistics, or plotting. Just describe your task or upload your data file.
• Memory: If activated, ChatGPT remembers preferences you set (like tone or formatting). Example: “Remember I want summaries in bullet points.”
• Vision: You can upload diagrams, charts, or photos. GPT-4 will describe or analyze what it sees.
• Live search (browsing): If enabled, GPT-4 can look up real-time information on the web, e.g., “Find recent publications from 2024 on this topic.”

Claude (Anthropic)

• Long document support: Claude 3 Opus can handle very long inputs—entire research papers, books, or datasets (up to 200,000 tokens).
• Multi-file upload: You can upload and cross-analyze up to 20 documents at once—e.g., multiple articles or experimental results.
• Vision support: Claude understands uploaded images but does not generate them.
• Project memory (Pro feature): Files added to a “project” can be used across multiple chats. Useful for persistent background knowledge.

Gemini (Google)

• File support: Upload documents (PDFs, CSVs, Google Docs) and ask questions about them.
• Multi-draft replies: Gemini shows several answer versions at once so you can compare and choose the best fit.
• Export integration: You can send results directly to Gmail, Google Docs, or Sheets with one click.
• Google Search integration: Ask for fresh information, real-time sources, or cite-able links directly inside the prompt.

Mistral (Mixtral 8x7B)

• Open-source model: Mistral is available for local use or integration into your own systems, offering more privacy and flexibility.
• Fast, efficient inference: Mistral models are optimized for speed and resource efficiency, ideal for on-premise or custom research environments.
• Toolchain support: While web interfaces are limited, Mistral integrates well with platforms like LM Studio, Ollama, or Hugging Face for advanced tasks.

Model Differences (2025 Quick Table)

Model	Context Window	Output Style	Features
GPT-4.1 Long (OpenAI)	128k	JSON/XML	Vision, Audio, Tool-Calling, Critique
GPT-4o (OpenAI)	128k	JSON/XML	Ultra-fast, Real-time voice
Claude 4 Opus	200k	XML/JSON	Parallel tools, Memory API
Gemini 1.5 Pro (Google)	1M	JSON	Vision/Audio, Google Search, RAG
LLaMA-3 70B (Meta)	8k-16k	JSON/XML	Open source, On-prem
Mixtral 8x7B	64k	JSON	Fast, Open, Apache-2

Tip: Proprietary models (OpenAI, Anthropic, Google) are easiest for beginners. Open-source models (Meta, Mistral) are best for privacy and custom setups.

Evaluation, Safety, and Ethics

• Always review AI answers—even structured ones! They may look perfect but still be wrong or misleading.
• RAG and CoT help avoid hallucinations, but always check citations and logic.
• Privacy: Never paste confidential or sensitive data into public AIs. Use local/open-source models for private work.
• Bias: Try different prompt phrasings and check for fairness.
• Evaluation: Use a checklist or have another person review answers for critical tasks.
• Self-critique: Ask the model to review/improve its own output, or compare multiple answers.

Quick-Start Checklist (Do & Don’t)

✅ DO:

• Separate prompt roles (System, Developer, User, Critique)
• Always put context/data first
• Be clear about the output format (and show an example)
• Use few-shot if results are inconsistent
• Add fresh info with RAG
• Double-check everything

❌ DON’T:

• Use vague prompts (“Summarize this”)
• Paste private data into public models
• Trust answers without checking
• Share step-by-step outputs with confidential research

Further Reading (May 2025)

• OpenAI Cookbook: Prompting Guide for GPT-4.1
• Anthropic Docs: Prompt Engineering Overview
• Google AI Blog: Gemini 1.5 Pro Context Benchmarks
• Wei et al. (2022): Chain-of-Thought Prompting
• Yao et al. (2023): Tree-of-Thoughts
• Mistral AI: Mixtral 8x7B Technical Report