Keeping ChatGPT on Track: How “Seeds” Prevent Context Loss in Long Conversations

Last night I ran into a problem that anyone who uses ChatGPT for complex, multi-step work will eventually face.

I was deep into a long technical conversation. One of those sprawling threads where you start with a small question and several hours later, you’re debugging naming conventions, verifying object properties, and refining a multi-step data ingestion pipeline. By that point, the conversation had gotten so long that responses were slowing down noticeably.

So I did what seemed logical: I started a new thread.

I wrote a brief note saying this was “continuing from the previous discussion” and assumed ChatGPT would pick up where we left off. After all, ChatGPT maintains a substantial amount of long-term user memory: facts about preferences, topics you work on, background knowledge, etc.

But here’s the key point:

Long-term memory isn’t the same thing as conversation-specific context.

And I had forgotten that.

In the previous thread, we had established some intricate details: specific IRIs in an ontology, naming conventions, modeling constraints, and the structure of a data ingestion workflow built in Prefect. None of that was saved as long-term memory, nor should it be—it was too specific and too volatile.

But in the new thread, ChatGPT didn’t have access to those details. That led to a subtle error: a property name was generated using a slightly different naming pattern than the one we had established earlier. It took a while to find the mismatch, and when we did, the cause was clear:

I had restarted the conversation without reestablishing the working context.

So I asked ChatGPT a natural question:

If a thread gets too long and I start a new one, how do I stop this from happening?

And that’s when ChatGPT introduced a concept that I now think everyone doing multi-step work with AI should know:

The Seed

A seed is a short text block that captures the essential facts needed to continue a project or thread. It’s not a summary of everything, just the non-negotiable pieces of context that ChatGPT must know to maintain continuity.

Think of a seed as a lightweight rehydration of context.

For example, a seed might include:

• The IRIs or naming conventions of an ontology you’re working on

• The structure of a JSON file expected by a data ingestion pipeline

• Constraints dictated by SHACL shapes

• Critical variable names in a codebase

• The roles of different components in a workflow (e.g., Prefect tasks, DAG structure, output formats)

In other words:

A seed is what you paste into the first message of a new thread so ChatGPT instantly knows where you left off.

It’s incredibly simple—and incredibly powerful.

Why Seeds Matter

ChatGPT’s long-term user memory is designed to remember things like:

• your preferences

• your coding style

• your ongoing interests

• the tools you use, and

• general background you’ve shared.

But it is not designed to carry forward facts like:

• the exact property name you added to an ontology at 11:43 PM last night,

• the updated CSV column structure in your experimental ingestion pipeline,

• a specific SPARQL pattern you’re refining,

• or the precise naming conventions you decided to apply within a project.

Those pieces of information live inside a thread, not in persistent memory. So if you start a new thread, ChatGPT can’t access them unless you bring them forward. That’s exactly what a seed is for.

Seeds + Projects = A Reliable Workflow

If you frequently work on the same project with ChatGPT, two things make your life easier:

1. Use a Project to group all related threads.

2. Keep one or more Seeds for that project.

Documents and Projects: How They Fit In

If you haven’t used them yet, ChatGPT Projects let you group related conversations, seeds, and uploaded files in a single workspace. Projects are great for organizing everything connected to a long-running effort—like a book, an ontology, or a software build.

Inside a project, you can also upload documents: Word files, PDFs, datasets, diagrams, or notes. These documents act as a reference library for the project, but they are not automatically part of the model’s working context. ChatGPT can only use a document if you attach it to a message or explicitly ask it to open the file.

This is the key distinction:

Documents store large amounts of background material. They’re useful for reference, versioning, or attaching when you want ChatGPT to analyze something specific.

Seeds store only the essential context that must load automatically. They ensure consistency across threads without requiring you to reopen or reattach anything.

In other words, project documents help with organization, while seeds help with continuity. Both are useful—but seeds are what prevent lost context when conversations get long or fragmented across threads.

For example:

• A Master Seed with global conventions

• A Naming Seed with your ontology or code naming rules

• A Data Ingestion Seed describing your workflow tools and file formats

• A Writing Style Seed if you collaborate with ChatGPT on documentation

Whenever you start a new thread—because the old one got long, or you want a fresh start—you paste the relevant seed(s) into your first message. ChatGPT instantly regains the critical project context without having to re-derive anything.Everything stays consistent, errors are less likely, and you avoid the “why is it suddenly generating different property names?” problem.

Here is an example seed ChatGPT and I created for the People Ontology, an ontology I use for various training purposes: People Ontology Seed.

A Small Technique with a Big Impact

This experience turned a frustrating debugging session into something surprisingly useful. Seeds are lightweight, easy to maintain, and incredibly effective at keeping ChatGPT aligned with your intentions across long-running, multi-threaded work.

If you rely on ChatGPT for serious technical projects, I strongly recommend adding seeds to your workflow. They make collaboration smoother, more predictable, and much more reliable.