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

Start Here: Make AI a Reliable Lab Partner for Your Biochemistry Work

AI for Biochemists (Prompt Course) shows researchers how to use large language models to plan experiments, parse literature, analyze data, and communicate results with greater clarity and speed. The focus is practical: you will learn how to structure requests so AI tools return helpful, reproducible outputs that fit real lab and computational workflows. The course combines wet-lab priorities with computational bioinformatics, protein science, safety, and scientific writing, so you can use AI responsibly across the full research cycle-from hypothesis to publication.

What you will learn

• How to frame research questions so AI can assist with experimental planning, controls, and assumptions.
• Ways to request structured outputs for kinetic modeling, pathway reasoning, sequence analysis, and data summaries.
• Strategies to critique suggestions, cross-check claims with sources, and transform AI drafts into citable, lab-ready material.
• Methods to connect outputs across modules-for example, moving from predicted protein function to kinetic experiments, pathway context, and interaction risks.
• Approaches for transparent documentation: prompt logging, versioning, and rationale tracking to support reproducibility.
• Ethical and safety practices, including data privacy, appropriate disclaimers, and validation before acting on AI-generated recommendations.
• Communication skills for grants and reports: turning technical findings into clear figures, tables, and narratives.

How the modules fit together

This is a cohesive curriculum rather than a set of isolated topics. Each area supports the others:

• Experimental design assistance sets the stage by clarifying goals, variables, controls, and measurement strategies.
• Protein function prediction proposes hypotheses that feed directly into enzyme kinetics planning and assay selection.
• Enzyme kinetics modeling informs parameter estimation and guides follow-up experiments and pathway questions.
• Metabolic pathway analysis places protein and enzyme insights into system-level context, pointing to regulatory nodes and potential off-target effects.
• Drug interaction predictions connect pathway context to safety considerations and candidate prioritization.
• Biochemical literature review supports every step with evidence checks, reference gathering, and synthesis.
• Statistical analysis modules help you request test plans, summarize results, and assess model fit in clear, auditable formats.
• Protein-DNA interaction analysis and genomic sequence analysis extend the same reasoning to motifs, variants, and regulatory effects.
• Bioinformatics data processing bridges raw files and interpretable outputs, with prompts geared for reproducible pipelines.
• 3D protein structure visualization and molecular structure analysis improve interpretation and communication of structural insights.
• Chemical safety information ensures that plans and reports reflect hazards, PPE, and handling guidelines.
• Biochemical simulation interpretation brings together dynamic models, sensitivity checks, and clear summaries for decision-making.
• Grant proposal writing assistance helps convert your technical plan into fundable aims and credible timelines.

Using the prompts effectively

Prompts are only as good as the context they provide. The course emphasizes a repeatable approach so your requests are clear and verifiable:

• State the objective, constraints, and success criteria before asking for suggestions.
• Provide relevant inputs (e.g., sequence segments, assay conditions, or result summaries) in a consistent format.
• Ask for structured outputs (checklists, stepwise plans, parameter tables, or bullet-point rationales) to ease review and reuse.
• Request evidence support and references where appropriate; verify citations and claims before use.
• Iterate: critique the first draft, add missing context, and ask for alternatives or edge cases.
• Keep a prompt log with version numbers so your lab-mates can reproduce the same result.
• Use domain-specific terms carefully; when uncertain, ask the model to define terms or list assumptions for quick inspection.

Skills by focus area

• Wet-lab planning: formulating hypotheses, identifying controls, anticipating failure modes, and preparing documentation that passes internal review.
• Protein science: interpreting sequence features, binding considerations, and structure-function implications with visual aids.
• Kinetics and modeling: organizing rate equations, discussing parameter estimation strategies, and presenting results with appropriate caveats.
• Pathways and systems: summarizing reactions, co-factors, and regulation; generating testable ideas that link pathway nodes to observed phenotypes.
• Safety and compliance: surfacing hazards, consulting recognized guidelines, and flagging missing information for lab review.
• Statistics: selecting suitable tests, clarifying assumptions, and presenting results with effect sizes and confidence intervals.
• Bioinformatics: cleaning inputs, choosing formats, and requesting outputs compatible with common tools.
• Communication: turning analyses into grant-ready aims, timelines, budgets, and response plans for reviewer concerns.

Who this course is for

• Graduate students and postdocs seeking a practical framework for AI-assisted research.
• Principal investigators and research leads who want consistent, auditable outputs from AI tools in their group.
• Industry scientists who need faster literature synthesis, option generation, and reporting without sacrificing quality.
• Educators building assignments that teach responsible use of AI in biochemistry.

Workflow examples you will be able to run (described in general terms)

• Move from a functional hypothesis to an assay plan, with statistical considerations and safety notes, then convert findings into figures and a draft report.
• Translate sequence or structure cues into testable protein function proposals, link to pathway context, and check for potential compound interactions.
• Request and refine kinetic models, summarize fit and limitations, and propose confirmatory experiments.
• Summarize key literature, extract methods, and map them to your constraints for quick decision-making.
• Prepare grant-ready narratives that connect aims, milestones, and risk mitigation to evidence and preliminary data.

Good practice and safeguards

• Fact-check claims and citations; never rely on a single generated reference without verification.
• Protect confidential data. Use redaction or mock examples for external tools where necessary, and follow your institution's policies.
• Keep human oversight for safety, ethical issues, and any decision that affects experimental risk or patient data.
• Report limitations. Encourage models to list assumptions and uncertainties; include these in your notes.
• Avoid overfitting conclusions to AI suggestions; seek independent support and validation experiments.

How the course saves time without cutting corners

By standardizing how you request plans, analyses, and summaries, you reduce back-and-forth and make outputs easier to check. Consistent formatting shortens lab meetings; templates help you move from brainstorming to documented action items; and iterative critique cycles encourage better decisions rather than rushed ones. The result is faster progress with clearer audit trails and fewer blind spots.

Deliverables and takeaways

• A reusable prompt playbook covering experimental design, bioinformatics, analysis, safety, and reporting.
• Reference formats for structured outputs you can paste into lab notebooks, ELNs, or code repositories.
• Checklists that help catch common pitfalls in kinetics, pathway reasoning, and interpretation of sequence or structure cues.
• Guidance for converting analyses into publication figures, method sections, and grant components.

Teaching approach

The course is practical and example-driven. Each module focuses on a real research need, then shows how to set up requests, critique outputs, and document the result. You will see how to adapt the same approach across wet-lab planning, protein science, sequencing, and structural analysis, with an emphasis on transparent reasoning and evidence support.

Limitations and how the course addresses them

• Hallucinations: learn tactics to request sources, compare with trusted databases, and cross-verify key facts.
• Ambiguity: practice narrowing requests, stating constraints, and asking for alternative models or interpretations.
• Bias and gaps: incorporate prompts that ask for missing viewpoints, failure modes, and dataset limitations.
• Reproducibility: log prompts and responses, annotate changes, and store final versions alongside results.

How this course supports different tools and file types

While the course centers on language models, the methods work alongside notebooks, spreadsheets, ELNs, and common file formats. You will see approaches for producing outputs that are easy to copy into statistical tools, molecular viewers, or sequence analysis workflows, keeping context and assumptions intact.

Outcomes you can expect

• Clearer experimental plans and faster iteration cycles, with better notes and citations.
• Improved coverage of literature and fewer missed references on key methods or safety issues.
• More consistent statistical summaries and parameter reporting for kinetic and pathway studies.
• Better integration between sequence, structure, and function reasoning in day-to-day work.
• Grant narratives that communicate feasibility, risk control, and measurable milestones.

Why this approach works

Biochemistry spans wet-lab practice, computation, and communication. A single prompting style rarely fits all three. This course shows how to shift style and constraints for each task while keeping a common spine: clear objectives, explicit assumptions, structured outputs, and verification. That gives you flexibility without losing rigor, and it turns AI from an occasional helper into a dependable part of your workflow.

Getting started

Begin with the foundation on making requests that are specific, testable, and easy to review. Then move through core research modules at your own pace. By the end, you will have a coherent set of practices, templates, and checks that help you plan, analyze, and communicate biochemistry work with confidence and accountability.