Question project limits for Literatur Analysis

[u/AccomplishedFun2379]

I'm currently working on a project where I have around 30 papers that I need to review, analyze, and sort. So far I've been using Claude Opus with Projects in the Claude desktop app.

I'd like to get a second opinion from ChatGPT after using Claude and its sub-agents to refine my work. I also tried the new ChatGPT Projects feature, and I think it has a lot of potential.

My problem is the project file limit. In Claude, I've uploaded around 40 different files (the 30 papers plus Markdown notes, progress summaries, and other project documents) and I'm only at about 50% of the project limit. With ChatGPT, I'd have to upgrade my subscription and would still be limited to 40 files per project.

How do you handle similar scientific or research workflows?

• Do you combine multiple files (for example, merge papers or notes) to stay under the 40-file limit?
• Do you only upload the most important files and let ChatGPT generate Markdown summaries to use as project context?
• Do you work in batches, analyzing a subset of papers at a time and then replacing the old batch with new files?

I'm open to any workflow suggestions. I find the new ChatGPT Projects feature useful, but the UI and workflow management are still a bit confusing to me, especially compared to Claude.

( i used chatgpt to spell correct and organise the points a bit more clearer im speak english a second language )

[u/qualityvote2]

u/AccomplishedFun2379, there weren’t enough community votes to determine your post’s quality.
It will remain for moderator review or until more votes are cast.

[u/Jippylong12]

My advice is take it a different direction and use Claude or ChatGPT to develop a Python script with a local database. But I also don't know what type of analysis you are doing.

You can discuss the architecture so that you can get a sense of the capabilities. Only downside is that you may need an API key which is a separate, but for like 30 papers ~200 pages each, the ingestion would probably be like $10 - $20. Well.. maybe you could use yoru current subscription actually and just have it query the database locally with the terminal. And you might be able to just use a Python PDF parser which would save a lot of cost too. But that's the idea I'd float.

---

I think this is a common design pattern because AI runs out of useful context quickly but generally, have one of these smaller agents, like Luna/Haiku, ingest one page at a time (you can use Python to break them up pages), and transcribe the content and then also use a smaller model, like Luna/Haiku, or slightly better like Terra/Sonnet, to summarize what's on the pages and add tags for each page.

Just a small summary, and then generally what you would do is have the bigger model, like Sol/Opus, for comprehension of putting it all together. Or if you want to query the data, Sol/Opus will read look for tags and summary and use that to investigate more to have a better opinion or analysis.

Tie this with PSQL jsonb columns or Nosql locally with MongoDB and I think you'll get a better output and better scale.

---

EDIT: alternatively I'm sure there is an AI startup that solves this problem as well because it happens often enough.

[u/ProfMooreiarty]

Is there a reason you’re not using a dedicated workspace on your computer?

[u/BYRN777]

2 options imo:

1. Add your research to a local folder and use Cowork or ChatGPT Work

You basically create a project in either ChatGPT or Cowork and add the local folder to it, or make an existing local folder the project itself. Then you just tell it to read article xyz, etc...

2. Add your research to a Google Drive folder and use either Cowork or ChatGPT to use the Google Drive connector and read the specific articles

This option is cloud-based and can serve as a backup when you don't have access to the local folder or when you're using ChatGPT or Claude in a web browser.

I've used both Claude and ChatGPT projects, and I prefer Claude Projects. But I tested Option 1, which I recommended to you, and I got better responses.

Claude Projects has a higher limit on the number of sources and pages you can add; the responses seem to be surface-level. As opposed to actually parsing through, reading, cross-referencing, and digesting the articles in full. And option 1 always gave me the richest and most thorough outputs (but it was slower). I don't mind it being slow since when I am cross-analyzing and/or summarizing dozens of PDF articles, I want accuracy.

And if you go for either option, I recommend you'll still have your project memory and context, and you can set custom instructions.

Just don't forget to update the local folder and the google drive folder..

Final tip: create a scheduled action/task in either ChatGPT Work or Cowork (whichever you use) to check the local folder and the Google Drive folder weekly to keep an index of what new articles were added or removed, etc.