How to effectively learn AI Prompting, with the 'AI for Research Scientists (Prompt Course)'?

Start using AI as a reliable lab assistant across your entire research cycle

AI for Research Scientists (Prompt Course) equips you to integrate AI across the full spectrum of research activities-from early ideation and literature synthesis to data analysis, simulation, writing, funding, and dissemination. The course is built around practical, research-grade workflows that help you move faster with higher clarity, while keeping scientific rigor, reproducibility, and ethics front and center.

What this course is about

This course assembles a cohesive set of prompt-driven workflows that map to the typical phases of research. Each module focuses on a core activity and shows how to frame, refine, and verify AI outputs for that activity. You will learn how to guide AI systems with clear goals, constraints, and formats so the results are actionable, auditable, and easy to integrate into your existing tools and lab practices.

• Surveying prior work and extracting structured insights
• Interpreting data and validating conclusions
• Formulating testable hypotheses and identifying confounders
• Designing experiments and simulations with appropriate controls
• Selecting statistical approaches and reporting assumptions
• Creating clear, publication-ready visualizations
• Drafting and refining proposals and manuscripts
• Handling ethical, legal, and social considerations responsibly
• Finding collaborators, spotting trends, and scouting technologies
• Evaluating algorithms and documenting results for reproducibility
• Exploring patent space and considering freedom-to-operate questions

How the prompts work together

Rather than isolated tricks, the course provides an integrated system. The same project can move through every module using consistent instructions, formatting standards, and checkpoints. You'll build a single thread of evidence-from initial literature mapping to final manuscript-that records sources, assumptions, choices, and risks. That coherence makes it easier to replicate results, share work with collaborators, and meet reviewer expectations.

• Continuity: Outputs from one module feed the next (for example, hypotheses inform experiment design, which then informs statistical planning and visualization).
• Consistency: Shared conventions for metadata, citations, units, and output formats reduce friction and errors.
• Quality gates: Each step includes checks for plausibility, bias, and compliance with field norms.
• Documentation: Prompts encourage logging of model, date, data sources, and decisions to maintain an audit trail.

What you will learn

• How to frame precise AI instructions that account for research goals, domain constraints, and required deliverables
• Ways to request transparent reasoning: assumptions stated explicitly, alternatives considered, and limitations acknowledged
• Strategies to improve reliability: cross-checking outputs, triangulating sources, and structuring validation steps
• Best practices for statistical thinking: matching tests to designs, surfacing assumptions, and avoiding common pitfalls
• Approaches to simulation and modeling that support sensitivity analysis and reproducibility
• Clear communication techniques for abstracts, figures, captions, and methods
• Ethical safeguards covering consent, privacy, data security, dual-use risk, and credit for AI assistance
• Methods for competitive analysis: trend tracking, technology scouting, and patent-aware planning
• Collaboration workflows for co-authoring, handoffs, and reviewer response letters

Using the prompts effectively

The course emphasizes a disciplined approach so your results are trustworthy and easy to reuse:

• Set scope: State the research question, boundaries, and success criteria up front.
• Provide context: Define key variables, units, constraints, and any field-specific conventions.
• Specify format: Ask for structured outputs (for example, bullet lists, tables, figure specifications, or stepwise plans) that you can paste into your lab notebook or manuscript.
• Request assumptions: Require the model to list assumptions, uncertainties, and potential confounds.
• Enforce citations: Require sources with enough detail to verify and retrieve items.
• Add checks: Include validation prompts that test consistency, replicate calculations, or contrast alternative interpretations.
• Iterate: Refine with constraints (sample sizes, budget, ethics approvals, timelines) to produce plans you can actually execute.
• Document: Record prompt versions, model identifiers, and any post-edits to support reproducibility.

Why this matters for scientific practice

Researchers face constant time pressure: surveying literature, restructuring datasets, choosing analyses, preparing visuals, and aligning writing with reviewer expectations. This course shows how AI can reduce overhead without sacrificing standards. You'll apply clear, repeatable methods to keep evidence trails intact, challenge conclusions, and make revisions faster.

• Rigor: Built-in guardrails encourage transparent reasoning and reproducibility.
• Speed: Structured prompts shorten routine tasks while preserving quality.
• Clarity: Outputs are organized for direct inclusion in lab notebooks, preregistrations, and manuscripts.
• Confidence: Verification steps help you trust-and if needed, revise-results before submission.

How the modules map to your workflow

• Idea to hypothesis: Map prior art, identify gaps, and phrase testable statements with measurable endpoints.
• Study planning: Translate hypotheses into protocols, controls, metrics, and sampling plans.
• Analysis and visualization: Select appropriate tests, prepare tidy data, and create interpretable figures and captions.
• Modeling and simulation: Explore mechanisms, run what-if scenarios, and document sensitivity analyses.
• Writing and funding: Structure proposals and manuscripts with clear aims, methods, risks, and impact statements.
• Ethics and compliance: Address privacy, consent, bias, dual-use risks, and acknowledgment of AI assistance.
• Dissemination and networking: Prepare summaries for diverse audiences and identify potential collaborators.
• IP and scouting: Review patent space, assess novelty, and track emerging methods and tools.

Good practice and guardrails

• Verification first: Treat initial outputs as drafts; cross-check against trusted sources and run independent calculations where possible.
• Data governance: Remove or anonymize sensitive data; follow IRB and institutional policies.
• Bias checks: Inspect datasets and interpretations for sampling bias, leakage, or inappropriate generalizations.
• Clear credit: Maintain an authorship and contributions record that includes AI assistance where relevant to journal or funder guidance.
• Reproducibility: Version prompts, datasets, and code; lock model settings when you need consistent regeneration of results.

Who this course is for

• Experimental, computational, and theoretical researchers in academia, industry, and government labs
• Graduate students and postdocs aiming to streamline repetitive work while adhering to field standards
• Principal investigators and research leads seeking consistent, auditable workflows across teams
• R&D professionals evaluating feasibility, market signals, and IP considerations alongside technical work

What you will take away

• A unified framework for using AI across literature, analysis, simulation, writing, funding, and dissemination
• Reusable structures for prompts, outputs, and validation so work is traceable and review-ready
• Checklists that keep ethics, compliance, and reproducibility visible at every stage
• Confidence to integrate AI into your daily practice without losing scientific standards

Learning format you can apply immediately

Each module focuses on outcomes you can use the same day. The emphasis is on clarity: what to ask, what to expect, how to verify, and how to slot results into your lab notebook, analysis scripts, and writing pipeline. The result is a cohesive set of practices that shorten feedback loops while maintaining quality.

A cohesive course that supports your next submission

Whether you are preparing a proposal, running a study, drafting a manuscript, or reviewing patent space, this course shows how to guide AI with clear instructions, obtain structured outputs, and check reliability. By the end, you'll have a repeatable way to apply AI across the entire research cycle, backed by documentation that stands up to scrutiny.