How to Use AI for Grant Writing: A Practical Guide for 2026

The grant writing community is split. On one side are researchers who view AI as a threat to authenticity -- a shortcut that produces generic, reviewable-at-a-glance prose that experienced evaluators can spot immediately. On the other side are PIs who have quietly integrated AI into their workflow and are producing more proposals, of higher quality, in less time.

Both sides have a point. AI-generated grant prose, used carelessly, reads like AI-generated grant prose. The hedging language, the tendency toward bullet-point structures, the loss of a distinct scientific voice -- these are real problems. But AI used strategically, at the right stages of the writing process and with the right human oversight, is a genuine productivity multiplier. The PIs who are winning the most funding are not the ones avoiding AI. They are the ones who have figured out where it helps and where it hurts.

This guide is a practical workflow for incorporating AI into your grant writing process while maintaining the authentic voice, scientific rigor, and strategic thinking that reviewers reward.

Where AI Helps: The Right Stages

AI is most valuable in the stages of grant writing that are labor-intensive but not voice-dependent. These are the structural, analytical, and iterative tasks that consume enormous time without requiring your unique scientific perspective.

Stage 1: RFP Analysis and Requirements Extraction

Before you write a single word of your proposal, you need to understand what the funder is asking for. A federal solicitation can run 40 to 120 pages, and buried within that document are specific requirements, evaluation criteria, formatting rules, and eligibility constraints that determine whether your proposal is compliant.

AI excels at this analysis. Upload the full solicitation document and ask the AI to:

• Extract all required proposal sections and their page limits
• Identify the evaluation criteria and their relative weights
• List all eligibility requirements
• Flag formatting specifications (font size, margins, file types)
• Summarize the program priorities and any special emphasis areas
• Identify required attachments and supporting documents

This task would take a human two to four hours of careful reading. AI can produce a comprehensive extraction in minutes, and the output serves as your compliance checklist for the entire writing process. Tools like Granted AI are built specifically around this RFP analysis workflow, parsing solicitations into structured requirements that guide the proposal development.

Human check required: Verify the AI's extraction against the source document for critical requirements. AI occasionally misinterprets ambiguous language in solicitations or misses requirements that are described in unusual locations (appendices, referenced external documents, linked websites).

Stage 2: Outlining and Structure

Once you understand the requirements, AI can help you build a detailed outline that maps your content to the funder's evaluation criteria. This is architectural work -- organizing your argument, ensuring every required element is addressed, and creating a logical flow across sections.

Effective prompting for outlining:

• Provide the AI with your extracted requirements list and ask it to generate a section-by-section outline that addresses each criterion
• Include your specific aims or project summary so the outline reflects your actual research, not a generic template
• Ask the AI to identify where in the outline each evaluation criterion is addressed, creating a traceability matrix between your content and the scoring rubric

The outline stage is where AI saves the most time relative to risk. A structural error at the outline stage -- missing a required section, misallocating page space, or failing to address a criterion -- compounds throughout the writing process. AI helps you catch these errors before you invest days in drafting.

Stage 3: First Drafts of Structured Sections

Some proposal sections are heavily structured and benefit from AI drafting. These include:

Budget justifications. Once you have your budget numbers, AI can generate the narrative justification text that explains each line item. Feed it the budget table and the funder's allowability guidelines, and it produces a draft that you edit for accuracy.

Facilities and equipment descriptions. These sections are formulaic -- describing laboratory space, computing resources, core facilities, and institutional infrastructure. AI can draft these from bullet-point inputs.

Biographical sketches and current/pending support. While you provide the raw information, AI can format it according to agency-specific templates (NSF biographical sketch format, NIH biosketch format).

Data management plans. AI can draft data management plans based on your data types, storage plans, and sharing intentions, following the specific format required by the funding agency.

Compliance narratives. Sections addressing human subjects protections, animal welfare, environmental impact, or responsible conduct of research follow predictable structures that AI handles well.

For these sections, the value of AI is speed, not creativity. You are not looking for AI to generate ideas. You are looking for it to convert your inputs into properly formatted narrative text that you can review and finalize.

Stage 4: Editing, Tightening, and Page Limit Compliance

This is where AI delivers perhaps its highest return on investment. You have a draft that is 15 pages long and the limit is 12. AI can identify redundancies, suggest cuts, and tighten prose while preserving meaning.

Effective editing prompts:

• "This section is 1,200 words and needs to be 800. Identify which sentences can be removed or combined without losing essential information."
• "Review this paragraph for hedging language ('may,' 'could potentially,' 'it is possible that') and suggest more direct alternatives."
• "This specific aims page has four aims. The funder expects three. Which aims could be combined, and how would you restructure?"
• "Check this budget justification against the following allowability guidelines and flag any line items that may not be allowable."

Stage 5: Compliance Checking

After your draft is complete, AI can perform a systematic compliance check -- comparing your finished proposal against the requirements extracted in Stage 1 and flagging any gaps. This is tedious work that humans do inconsistently when they are exhausted from weeks of writing.

Ask the AI to verify:

• Every required section is present and within page limits
• Evaluation criteria are explicitly addressed (not just implied)
• Formatting requirements are met (margins, font, spacing)
• Required attachments are accounted for
• Budget totals align with stated limits
• Personnel effort allocations sum correctly

Where AI Hurts: What to Keep Human

The sections that reviewers scrutinize most closely for authenticity, insight, and strategic thinking are the sections where AI assistance should be minimal. Letting AI draft these sections produces the generic, detectable prose that damages proposals.

Your Scientific Voice and Argument

The core of your proposal -- your specific aims, your significance section, your approach -- is where your scientific thinking needs to be unmistakably present. Reviewers evaluate not just what you propose but how you think about it. The logical connections between your preliminary data and your hypotheses. The way you frame risk and mitigation. The judgment calls about what to include and what to leave out.

AI can help you refine this prose after you have written it. But if AI generates the initial argument, the result tends toward a middle-of-the-road, cover-all-bases narrative that lacks the specificity and confidence that characterize strong proposals. The difference is detectable. Experienced reviewers read hundreds of proposals, and they notice when a significance section reads like a synthesis of the literature rather than an argument from a scientist who has a specific point of view.

Relationship-Specific Content

Letters of collaboration, consortium agreements, mentorship plans, and consultant descriptions need to reflect genuine relationships. If you propose a collaboration with Dr. Smith's lab for mass spectrometry analysis, the description of that collaboration should reflect actual conversations you have had with Dr. Smith about the work -- the specific instruments available, the timeline for sample processing, the costs, the intellectual contribution Dr. Smith will make.

AI cannot know what you discussed with Dr. Smith. If you ask it to draft a collaboration description, it will produce something plausible but generic. That genericness signals to reviewers that the collaboration may be superficial.

Proprietary Methods and Preliminary Data

If your proposal's competitive advantage depends on a novel method your lab developed or preliminary data you have generated, the description of that work needs to come from you. AI has no access to your unpublished results and cannot accurately describe what you found, what the limitations were, or what the data implies for the proposed research.

You can use AI to help format and present your preliminary data descriptions after you have written them. But generating those descriptions from scratch should be entirely human.

Innovation and Significance Arguments

The "why this matters" argument is where your expertise and judgment are irreplaceable. Why is your approach innovative compared to what the field has tried before? Why is the problem significant right now rather than five years from now? What makes your specific angle on this question better than alternative approaches?

These arguments require deep knowledge of the field, awareness of what reviewers in your study section value, and strategic judgment about positioning. AI can help you articulate these arguments more clearly after you have formulated them, but it should not be the source of the arguments themselves.

Prompt Engineering for Grant Writing

The quality of AI output in grant writing depends almost entirely on the quality of the input. Vague prompts produce vague proposals. Here are the prompting techniques that produce the best results.

The Solicitation-Anchored Prompt

Always include the relevant solicitation text in your prompt. Instead of asking "Write a broader impacts section for an NSF proposal," provide:

"Here are the NSF broader impacts criteria from the current solicitation: [paste criteria]. Here is my proposed research: [paste specific aims]. Here are the broader impacts activities I have planned: [describe your activities]. Draft a broader impacts section that explicitly addresses each criterion while describing these specific activities."

The solicitation context constrains the AI's output to what the funder actually wants rather than what the AI imagines a broader impacts section should contain.

The Role-Specific Prompt

Tell the AI who it is and who the audience is:

"You are a grant writing consultant with expertise in NIH R01 proposals. Your client is an early-career investigator in computational neuroscience applying to NINDS. The study section is likely to include reviewers with expertise in neuroimaging, computational modeling, and clinical neurology. Draft a significance section that addresses this audience."

Role specification produces more focused, appropriately pitched output than generic prompts.

The Iterative Refinement Prompt

Never accept the first AI output as a draft. Use iterative prompting to refine:

1. "Draft a one-paragraph summary of Aim 2 based on these details: [provide details]."
2. "The tone is too cautious. Make the language more assertive and specific. Replace hedging phrases with direct statements."
3. "Add a sentence connecting the expected outcome of Aim 2 to the rationale for Aim 3."
4. "This paragraph is 180 words. Reduce it to 120 without losing the key points about the novel statistical approach."

Each iteration brings the output closer to what you need. Four rounds of refinement typically produce usable draft text that requires only final human editing.

The Reviewer Simulation Prompt

One of the most valuable uses of AI in grant writing is simulating reviewer feedback before submission:

"You are an NIH study section reviewer with expertise in cancer biology. Review the following specific aims page as if you were writing a critique for the summary statement. Identify weaknesses in the rationale, gaps in the logic, overly ambitious timelines, and any claims that are not supported by the preliminary data described."

This simulated review will not catch everything a real reviewer would, but it consistently identifies structural weaknesses, unsupported claims, and logical gaps that the PI missed because they were too close to the work.

A Complete Human+AI Workflow

Here is what a full proposal development cycle looks like when AI is integrated at the right stages.

Week 1: Analysis and Planning (AI-Heavy)

• Upload the solicitation to AI for requirements extraction and compliance checklist generation.
• Use AI to map evaluation criteria to proposed content areas.
• Draft your specific aims page entirely by hand. This is the intellectual core of your proposal and must reflect your thinking.
• Use AI to critique your specific aims page from a reviewer's perspective.
• Revise the specific aims based on AI and human feedback.

Weeks 2-3: Core Drafting (Human-Heavy, AI-Assisted)

• Write the significance and innovation sections yourself. These are voice-dependent sections.
• Write the approach section yourself, but use AI to help format methods descriptions and check for logical consistency between aims.
• Draft the preliminary data presentation yourself (AI cannot know your unpublished data).
• Use AI to draft structured sections: facilities description, equipment list, data management plan, budget justification.
• Use AI to generate first drafts of compliance sections (human subjects, vertebrate animals).

Week 4: Integration and Refinement (AI-Assisted Editing)

• Use AI to check consistency across sections (are the specific aims described the same way in the abstract, the approach, and the timeline?).
• Use AI to tighten prose and meet page limits.
• Use AI to perform compliance checking against the extracted requirements.
• Have colleagues read the proposal for scientific feedback (human review remains essential).
• Use AI to simulate reviewer critiques and identify remaining weaknesses.

Week 5: Finalization (Human-Heavy)

• Make final revisions based on colleague and simulated reviewer feedback.
• Write the project summary and abstract yourself (these are read first and set the tone).
• Finalize all formatting manually.
• Conduct a final human read-through of the complete proposal.
• Submit.

Maintaining Authenticity

The most important principle in using AI for grant writing is that the final product must sound like you. Not like AI. Not like a generic scientist. Like you -- with your specific expertise, your particular way of framing problems, and your distinctive scientific judgment.

This means:

Read every AI-generated sentence and ask: would I say this? If the answer is no, rewrite it. AI tends toward certain patterns -- passive voice, hedge words, balanced-sounding constructions that avoid taking a position. Your proposal should take positions.

Preserve your technical vocabulary. AI sometimes replaces precise technical terms with more general language to improve readability. In a grant proposal, precision matters more than accessibility (except in broader impacts sections). If you use a specific term of art in your field, keep it.

Maintain argumentative structure. AI tends to organize information descriptively (here is what we know, here is what we do not know, here is what we propose). Strong proposals are argumentative (here is why this problem matters now, here is what is wrong with current approaches, here is why our approach is better). If AI flattens your argument into a description, restructure it.

Keep the "so what" human. The implications of your research -- why it matters for the field, for patients, for policy, for society -- need to come from your understanding of the landscape. AI can state implications, but it cannot weigh them with the judgment of someone who has spent years in the field.

The goal is not to hide AI use. It is to use AI in a way that makes your proposal better without making it sound like someone else's. When done well, the result is a proposal that is more thorough, more compliant, and more polished than what you would produce alone -- but still unmistakably yours.

Keep Reading

• Can AI Write a Grant Proposal?
• Best AI Grant Writing Tools in 2026
• How to Write a Grant Proposal
• Granted AI Features

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