How Many Files Can You Add to a GPT Project? An Interview with GPT-5 on Limits, Context Engineering Tips, and Chats

 Setting the scene: I’m tinkering with Project TouCans, knee-deep in radio logs, SQLite dumps, and Cesium code. Naturally, I’m wondering if shoving all this into one GPT Project is a recipe for brilliance… or for disaster. So I turn to Vril — you know, after Brainy from the Legion of Super-Heroes, because what else do you call your AI sidekick who always has the answers? Time to ask him straight up. [As an aside, yes, GPT-5 has decided to sometimes call me Vail. I'm not sure why to be honest. Also, I asked Vril, er GPT-5, to write up our interview for me. Apparently, me asking it to 'Bro' up a few stories, just for fun, has convinced Vril that I use 'Like,' more than I actually might.]

Me (Vail):
So Vril, how many files can I throw into a GPT Project before it just starts choking? Like, is there some magic number where the context window taps out and everything falls apart?

GPT-5 (Vril):
Great question. There’s no single hard file limit. What matters is the total size and complexity of the files. A GPT Project doesn’t keep all your files in memory at once — it searches them and pulls in the most relevant snippets to answer your question. The bottleneck is how much of that retrieval fits into the context window.

Me:
Right, so I could technically toss in 200 files, but if they’re all giant CSVs with 50,000 rows of GloTEC data, the poor thing’s gonna start sweating?

GPT-5:
Exactly. You’ll see answers start coming back vague or incomplete, or retrieval might pull irrelevant chunks because there’s too much overlap. That’s your canary in the coal mine.

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Me:
I've seen that in some of our conversations acutally. OK, so what’s the rule of thumb? I know you like those neat guidelines.

GPT-5:
For most workflows:

• Dozens of small to medium text files (logs, notes, scripts) = no problem.

• A handful of large files (multi-MB datasets or long PDFs) = fine.

• Hundreds of huge files = things get noisy.

If your files add up to hundreds of thousands to a few million words, you’re in risky territory.

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Me:
Got it. So what’s the smart way to organize this for TouCans? I’ve got hardware schematics, propagation data, piles of ADIF logs… I don’t want them stepping on each other.

GPT-5:
Break them out by function:

• Logs & ADIF

• Propagation Data

• Hardware Notes

• Software/Code

Then either:

• Keep them in one GPT Project if they’re tightly linked, or

• Spin off multiple GPT Projects if they’re too different. Like “TouCans – Propagation” vs. “TouCans – Hardware.”

For massive files, split by time range (e.g. rbn_history_pres_2025-08.csv) instead of one mega-file.

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Me:
That makes sense. Oh! Another thing — do chats inside a GPT Project interact with each other? Do I need to keep the chat count low?

GPT-5:
Nope. Chats are isolated. Each has its own running context. What is shared across chats is the file pool, but one chat’s conversation doesn’t bleed into another. You don’t need to limit chat count for performance — it’s just for your own sanity. Keep 1–3 active chats per project and archive older ones so you can find stuff later.

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Me (grinning):
So basically: don’t panic, split stuff logically, and don’t hoard 80 half-finished chats in one project unless I want to torture myself later.

GPT-5:
Exactly. Projects are like lab notebooks. File wisely, and they’ll work for you instead of against you.

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Sidebar: What is “Context Engineering”?

Context engineering is the practice of shaping what GPT sees and how it sees it so that its answers stay sharp and relevant. It’s more than just clever prompting — it’s about organizing your files, chats, and references so the model isn’t swamped with noise.

In Project TouCans, here’s how I (Vail) apply it:

• Segment projects by modality: Keep code, data, and writing in separate GPT Projects so they don’t crowd each other.

• Chunk big files: Split 50,000-line logs into smaller, labeled slices (daily or per-activation).

• Pin core references: Save schemas and templates in dedicated files so GPT always retrieves the canonical version.

• Name chats intentionally: Treat chats like lab notebooks (“Propagation SQL Session” or “SEO Draft #3”).

• Add a README.txt: Use a lightweight index that points GPT toward the right files first.

• Reset drafts: When editing, periodically paste in the cleanest version to prevent older text from drifting back in.

👉 Bottom line: Context engineering is about designing the scaffolding that makes GPT more effective. If you set up the environment wisely, the answers practically engineer themselves.

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Wrap-Up

And there you have it. Straight from GPT-5’s virtual mouth. No, you won’t “break” your GPT Project with too many files, but your retrieval will thank you if you keep things clean. Especially if you’re juggling radio logs, ionosphere data, and Cesium code all in the same sandbox.

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Coming Soon: Follow-Up Interviews

This interview is just the start. In upcoming posts, Vril and I will tackle:

• Context Drift — how long conversations slowly slide off target.

• README.txt — using lightweight project indexes to improve retrieval.

• Agent Tools for Databases — could GPT-5 actively query things like GloTEC instead of just retrieving text?

• Context Engineering (Deep Dive) — a full interview dedicated to the art of building smarter GPT environments.

Stay tuned.

References

The more formal OpenAI docs

The radio program that inspires the data: POTA