I. Framing the Interface

  • LLM as Partner, Not Oracle:
    Treat the model as a collaborator in reasoning, not a replacement for intuition or proof.
  • Session Structure:
    Start each session with a clear research question or field goal.
    Use the AI for expansion (generation, brainstorming, translation) and compression (refinement, error-checking, structuring).

II. Input Optimization

  • Precision Prompts:
    Ask multi-step, context-rich questions.
    Provide relevant definitions, background, or notation in the initial prompt.
  • Iterative Dialogue:
    Treat each AI reply as a draft to refine—probe, clarify, and re-prompt for increased accuracy or structural alignment.

III. Authorship and Control

  • Maintain the Human Voice:
    Use your own language, intuition, and style throughout.
    Edit AI outputs directly; never present unedited AI prose as final.
  • Decouple AI Output from Final Form:
    Each result, proof, or derivation generated by the AI should be checked, rewritten, and placed in your own narrative framework.

IV. Structural Use-Cases

  • Live Derivation:
    Use AI to expand intermediate steps in proofs, especially for routine calculations or checking known theorems.
  • Field Mapping:
    Employ the model to cross-link concepts across algebra, topology, analysis, and physics.
  • Meta-Translation:
    Ask for explanations in multiple registers—formal, intuitive, visual—to find the resonance that fits your own style.

V. Documentation & Reproducibility

  • Timestamp and Archive:
    Save significant AI-generated insights as annotated PDFs, Markdown files, or blog entries, dated and contextually labeled.
  • Keep Source Chains:
    Record the input prompt, AI reply, and your post-edits for major results; this builds provenance and enables reproducibility.

Disclosure Clause:
Add a methods note:

“This work was produced in active collaboration with a large language model under the direction of the author; all results have been independently verified and integrated into the author’s narrative voice.”

VI. Limitations and Pitfalls

  • Don’t Over-delegate:
    Use AI for scaffolding and pattern expansion, but never for final theorem selection, conjecture claims, or the main proof step.
  • Spot-Check for Hallucination:
    Systematically check all claims, especially in deep technical sections.

VII. Future Expansion

  • Community Sharing:
    Share protocols and insights—help others see how to get from “prompt” to “publishable result.”
  • Iterate the Method:
    As new model capabilities appear, re-examine which parts of your process benefit from AI augmentation and which require pure human insight.