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Why I Don’t Keep Session Logs for AI Collaboration

How Session Logs Quietly Destroy Long-Term Context

If you work with AI long enough, keeping session logs feels like the obvious next step.

It sounds responsible.
It sounds safe.
It sounds like “good documentation practice.”

I used to think so too.
I don’t anymore.


Session Logs Feel Helpful — Until They Aren’t

Session logs promise many things:

  • continuity across sessions
  • traceability of discussions
  • accountability for decisions

They look like the perfect solution for long-term AI collaboration.
But over time, I noticed a pattern.

The more session logs I kept,
the harder it became to reason about the past.

Not easier.


The Problem Is Not Volume — It’s Ambiguity

The core problem with session logs is not that they are long.
It’s that they mix incompatible states.
Inside a typical session log, you’ll find:

  • tentative ideas
  • rejected options
  • partial reasoning
  • temporary assumptions
  • final decisions

All flattened into one timeline.
Humans can sometimes separate these mentally.
AI cannot.

To an AI, everything written is equally real.


Session Logs Turn “Thinking” Into “Facts”

Session logs have a dangerous side effect:

They turn thinking into history.
An idea that was:

  • explored briefly
  • rejected explicitly
  • never implemented

still exists in the log.
Weeks later, when AI scans past context, it cannot reliably tell:

  • “This was considered and rejected”
  • from
  • “This was a valid alternative”

The result?

Old ideas come back.
Decisions quietly erode.
Context drifts.


Why This Gets Worse Over Time

The longer a project runs:

  • the more session logs accumulate
  • the more abandoned paths exist
  • the more contradictory context is visible

Eventually, the AI starts to reason like this:

“There are multiple plausible interpretations of the past.”

That is fatal for long-term collaboration.

You don’t want plausible pasts.
You want a single authoritative one.


Decisions and Exploration Must Not Share a Container

This is the key rule I learned the hard way:

Exploration and decisions must never share the same container.

  • Exploration is allowed to be messy
  • Decisions must be clean

Session logs violate this rule by design.
They preserve everything.


What I Use Instead

I split the world cleanly:

1. Decision History (Canonical)

Only finalized decisions enter history.

  • what was decided
  • why it was decided
  • what changed
  • what remains open

Nothing else survives.
This history is small, slow, and stable.


2. In-Progress Space (Disposable)

All exploration lives here:

  • brainstorming
  • failed attempts
  • partial designs
  • abandoned ideas

This space is allowed to be chaotic.
It is not consulted when reasoning about the past.


Why “current.md” Is a Trap

Many systems introduce a current.md file
to capture “what’s happening now.”

This seems reasonable.
It isn’t.

A current state file:

  • freezes a temporary moment
  • outlives its validity
  • quietly becomes historical by accident

When a session ends unexpectedly,
current.md lies.

I don’t allow such a file to exist.


How AI Benefits From This Absence

Without session logs:

  • AI sees fewer but stronger signals
  • reasoning becomes sharper
  • past decisions stop drifting

When I ask AI to “check past decisions,”
it sees only:

  • what survived
  • what mattered
  • what was intentionally kept

Nothing else competes for attention.


This Is Not About Less Documentation

This is not minimalism.
It is precision.

I document less so that what remains can be trusted.

Long-term context is preserved
not by recording more,
but by allowing less to survive.


Closing

Session logs feel safe.

But safety comes from authority, not completeness.
For long-term AI collaboration, history must be strict,
or it will quietly collapse under its own ambiguity.


This article is part of the **Context as Infrastructure* series —
exploring how long-term AI collaboration depends on structure, not memory.*

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