What Is Compaction? Why Long AI Chats Forget Earlier Details
Have you ever been deep into a long AI chat and noticed it quietly ignoring a rule you set at the very start? You end up pasting the same instructions again, and sometimes one missed condition means redoing the whole output.
Compaction is a way of shrinking a long AI conversation so it can keep going with only the key state needed for the next step. Here, daglo explains how compaction works, based on OpenAI's official documentation, along with habits that keep long AI chats on track.
What is compaction? Compressing a conversation to keep it going
Compaction compresses everything said so far once a conversation gets too long. OpenAI's docs describe it as a way to reduce context size while preserving the state needed for later turns.
In the OpenAI API, compaction comes in two forms.
| Type | How it works | What it means |
|---|---|---|
| Automatic compaction | The server compresses the context once it passes a set threshold | The conversation continues without a separate request |
| Manual compaction | The app asks for compaction when it chooses | You control when compression happens |
One detail stands out: the compacted result isn't a readable summary. It's encrypted data that isn't meant to be interpreted by people. Since you can't open it to see what was kept and what was dropped, the only way to check it is to see whether the work still carries on correctly afterward.
Why long AI chats forget earlier details
An AI can only consider a limited amount of text at once. That limit is called the context window. As a chat grows, many tools trim or compress the earlier parts, so details start to blur.
Longer chats also mean more text to process, which affects speed and cost. Compaction helps, but compression always leaves something out, so conditions you set early can slip away. If the AI keeps missing a rule you've repeated several times, take it as a sign the conversation has grown too long.
OpenAI's docs say compaction carries forward key prior state, and suggest verifying the behavior for your use case before relying on details from earlier turns.
Three habits for keeping long chats on track
For long tasks, results are steadier when you hand over only what's needed instead of piling everything into one chat, and when you clearly flag changes in direction.
- Write down the rules that must survive: keep must-follow conditions and approaches that already failed in a separate note, and pass them along when you start a new chat.
- Be specific when referencing an earlier chat: some tools let you point to a previous conversation and have the AI read it. When you do, name what to bring over: confirmed conclusions, evidence, failed attempts, and assumptions that haven't been checked yet.
- After changing direction mid-task, check what already happened: the OpenAI API has a feature called mid-turn steering that lets you send new instructions before a response finishes. The official docs note that it doesn't undo actions already taken. If you change course, confirm what was done before the change.
A summary is compression, so keep the original
Whether it's compaction or a summary a person wrote, a summary is a shortened version of the original. For anything that has to be exact, like contract terms, numbers, or who decided what, keep the original alongside the summary so you can check it later.
The same goes for meetings and calls. daglo keeps the full transcript of your recording and the AI summary together in one board. If something in the summary raises a question, you can check it against the transcript right away, and Board Chat lets you ask things like "List only the schedule changes from the last meeting," based on the whole record. If you want the same format every time, save your summary instructions in My Templates, and daglo will summarize every new recording that way automatically.
A transcript is even easier to revisit when it shows who said what. Speaker ID remembers the voices of the people in your meetings and fills in their real names instead of "Speaker 1" from the next recording on. daglo is the first service in Korea to offer this to all users. Learn more about Speaker ID
How does compressing long chats affect cost? Read our guide on measuring AI cost per successful task instead of per token.
Source: OpenAI Compaction
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