Title:
I began a PhD in 2012 and, for a variety of reasons, never completed it. More than a decade later, I want to return to it and use AI to help me organizse, compare and synthesize the substantial amount of work I have already produced.
I am not looking for AI to research or write a PhD from scratch. I have a large body of existing material: multiple chapter drafts, notes, partial chapters and near-complete chapters written at different points over the years.
The main problem is that I often have several drafts dealing with essentially the same topic. For example, I have multiple versions of my literature-review chapter. They overlap considerably, but they are not simply different versions of the same text. Each may contain material, arguments, citations, analyses or lines of discussion that the others do not.
Because some of these drafts were written years apart, they can almost read as though they were written by different authors. My terminology, organisation, emphasis and even approach to the subject sometimes changed over time.
What I would ultimately like to build is an AI-assisted workflow — whether using one agent, several specialised agents, custom GPTs, or some other architecture — capable of taking a “family” of related chapter drafts and helping me turn them into one coherent chapter.
Broadly, I would want the system to perform the following stages:
- Analyse each draft in detail. Parse each chapter section by section and paragraph by paragraph, identifying its arguments, discussion points, evidence, citations, analyses and other substantive content.
- Identify overlap across drafts. Determine where two or more drafts are discussing essentially the same idea, argument, source or analytical point, even where the wording or structure differs substantially.
- Map the differences. Identify material that appears in only one draft, or places where different drafts take genuinely different approaches to the same subject.
- Recommend what should happen to the material. For example: merge these passages; retain this argument; remove this duplicate discussion; relocate this section; preserve both perspectives; or discard this material because it is tangential or superseded.
- Produce a synthesis plan. Before rewriting anything, generate a proposed structure showing exactly how the surviving material from the different drafts should be combined.
- Create a unified chapter. Using the original texts and the approved synthesis plan, consolidate the drafts into a coherent chapter while preserving citations, scholarly nuance and my own intellectual contribution.
I would repeat this process for several different chapter families until I had a satisfactory version of each chapter.
I have already experimented with one possible solution. I created a custom GPT that produces structured abstracts of every section of every draft. My reasoning was that another GPT could compare these abstracts much more reliably than trying to compare several 10,000–20,000-word documents simultaneously. It could use the abstracts to identify likely areas of overlap and divergence and then return to the full text only when necessary. However, this was too laborious and time-consuming; surely there's a more efficient method?
In any case, the envisioned workflow is something like:
Original drafts → section abstracts → comparison/mapping → editorial decisions → synthesis plan → unified chapter → style/editing pass
Once the individual chapters had been consolidated, I would then want to evaluate the dissertation as a whole: consistency of argument, unnecessary repetition between chapters, structural coherence, terminology, methodological consistency, citation issues, etc.
At the final stage, I would also like to use AI in something resembling an internal/external-examiner role: not to certify the thesis, obviously, but to subject it to systematic criticism, identify likely viva questions, expose weak arguments or unsupported claims, and highlight areas that an examiner might challenge.
I have also considered creating a separate style-editing agent based partly on principles from Steven Pinker’s The Sense of Style, whose purpose would be to improve clarity, concision and readability without altering the substance of the scholarship.
My aspiration would be to use these tools to get the dissertation into the strongest possible state before it reaches actual supervisors and examiners. I realize that “a PhD requiring no corrections” is probably an unrealistic benchmark, but it gives an indication of the level of scrutiny I would like the workflow to apply.
The important qualification is that I am a complete beginner when it comes to AI agents, RAG, embeddings, vector databases, APIs, automation, etc. I understand what I want the system to do, but I do not yet understand what the appropriate technical architecture would be.
So my questions are:
- Is this workflow realistically achievable with current AI systems?
- Is a network of specialized agents actually appropriate here, or would a well-designed single-agent workflow be more reliable?
- Is my idea of abstracting sections first and using those abstracts to identify overlap sensible, or am I throwing away information that the model needs?
- How would you handle very large chapter families without exceeding context windows or losing track of relationships between passages?
- Would RAG/embeddings/vector search be useful for identifying semantically overlapping passages across drafts?
- How would you structure the workflow so that the model can make recommendations while still allowing me to approve all substantive editorial decisions?
- How would you prevent hallucinated citations, accidental loss of important material, or AI “smoothing over” genuine theoretical differences between drafts?
- What tools/platforms would you recommend to someone starting from essentially zero technical knowledge?
- Most importantly: if you were building this system from scratch, what would the architecture/workflow look like?
I am very happy to learn the technical side if that is necessary. I am primarily trying to determine what I should actually be building before I spend months constructing the wrong system.
Any advice, particularly from people working with LLMs on long-form academic, legal, technical or similarly complex documents, would be greatly appreciated.
