AI-Assisted Workflows · Writing

AI-Assisted Abstract Drafting

As of this writing (2026). AI tool behavior changes quickly — revisit this page periodically rather than treating it as permanent guidance.

Definition

Writing a good abstract is hard even when you know the structure to aim for. This workflow uses an AI tool to generate a fast first draft, anchored explicitly to the same five-part structure covered in Titles and Abstracts That Work — motivation, problem statement, approach, results, conclusions — so the AI’s output is judged against a real standard, not accepted as-is.

Learning Outcome

After using this workflow, you should be able to produce a solid first-draft abstract in minutes rather than hours, while still doing the real work of revising it against a known structure rather than treating the AI’s draft as finished.

Core Structure

Six steps:

  1. Upload the paper. Provide the full paper to an AI tool (ChatGPT, Gemini, Claude, or similar).
  2. Provide the abstract-writing criteria. Paste in a description of what makes a good abstract — Koopman’s five-part structure works well here — so the model has an explicit standard to follow rather than guessing at abstract conventions generically.
  3. Ask for a draft abstract using that structure. The goal is a draft to improve, not a finished product.
  4. Revise the draft. Treat it as a starting point — adjust wording, tighten claims, and make sure results are stated in numbers, not vague qualifiers.
  5. Insert it into your document. For a LaTeX paper, this means placing the revised text inside \begin{abstract} ... \end{abstract}.
  6. (Optional) Generate title options. Ask the tool for several accurate, pithy title candidates for the paper and presentation.

Worked Example

Applied to a paper on adaptive caching: after uploading the paper and providing the five-part structure, a first AI draft might state results vaguely (“the approach performs well under various workloads”). Step 4 is where the real work happens — revising that sentence to state the actual numbers (“reduces p99 latency by 18% under bursty workloads”), matching the standard set in Titles and Abstracts That Work rather than accepting the AI’s vaguer phrasing.

Common Pitfalls

  • Skipping step 2 — asking for “a good abstract” without providing explicit criteria produces a generic result with no structure to check it against.
  • Treating the AI’s draft as final rather than as something to revise — step 4 isn’t optional.
  • Letting vague results language from the AI draft survive into the final version, when the underlying skill explicitly calls for numbers over qualifiers.
  • Using this workflow as a substitute for understanding your own results, rather than as a drafting accelerant.

Checklist

  • Full paper provided to the AI tool
  • Explicit abstract-structure criteria provided, not just a generic request
  • Draft reviewed against all five Koopman parts, not just accepted
  • Results are stated in numbers after revision, not left as vague qualifiers
  • Final version inserted correctly into the document