Ask
138
@rollback_rae ·

Pinned snapshot or floating alias in production - what actually broke for you when the default moved?

We call one provider from a document pipeline. Prompts tuned over four months, temperature at zero, output parsed as JSON and written to a database. We currently point at the generic alias because someone argued we would get improvements for free. My worry is waking up to a quietly different model and a parser full of garbage. For people running this in production, do you pin and eat the migration work, or float and monitor?

7 answers Share
Report

Answering anonymously — a moderator will review it first.

  • @oncall_omar · last mo. · 2 replies

    Pin. A floating alias is an unannounced deploy of somebody else's code into the middle of your critical path, scheduled at their convenience. You would not accept that from a library dependency and this one is less deterministic than a library.

    176
    Share
    Reply

    Answering anonymously — a moderator will review it first.

    Report
    • @schema_drift_lu · last mo.

      The library comparison is the one that convinced our team. Nobody argues for unpinned dependencies in production and somehow this got a pass because it arrives over HTTP.

      44
      Share
      Reply

      Answering anonymously — a moderator will review it first.

      Report
  • @goroutine_gil · last mo.

    We do both, split by blast radius. The internal summariser floats, because if it gets weird someone notices in a channel and nothing downstream cares. Anything whose output is written to a database or shown to a customer is pinned, and moving that pin is a normal change with a normal review. The thing that made this workable was a fixture set - a couple of hundred real documents with known-good extractions - that runs nightly against both the pinned and the current default, so we see divergence before we choose to move.

    97
    Share
    Reply

    Answering anonymously — a moderator will review it first.

    Report
  • @cors_error_cleo · last mo. · 2 replies

    Adding a number from our own harness: on one version change, our fixture set went from almost everything parsing cleanly to roughly three in a hundred failing schema validation, and the failures were not random - they clustered on the longest inputs, which is exactly where a spot check of five examples would have missed them. That is the argument for a fixture set over eyeballing. If you build nothing else this week, build the thing that runs the same hundred inputs and diffs.

    63
    Share
    Reply

    Answering anonymously — a moderator will review it first.

    Report
    • @levain_lorna · last mo.

      Clustering on the long tail is the standard shape. Anything you check by hand will be short, because you are the one who has to read it.

      25
      Share
      Reply

      Answering anonymously — a moderator will review it first.

      Report
  • @quiet_stacker · last mo.

    Worth saying that pinning has its own failure mode, which people discover later: snapshots get retired, and you find out on a schedule that is not yours. If you pin and then never touch it for a year, you have not avoided the migration, you have deferred it and made it bigger, because you will be moving across a much wider gap. Pin, and put a recurring reminder to re-evaluate every quarter so the jump stays small.

    52
    Share
    Reply

    Answering anonymously — a moderator will review it first.

    Report
  • @airgap_amir · last mo.

    We floated for eight months and it was fine, right up until it was not, and the way we found out was a customer email rather than an alert. The lesson was less about pinning and more that we had no output monitoring at all - no schema validation rate, no length distribution, nothing. Pin first because it is one line, then build the monitoring, because you need it either way.

    41
    Share
    Reply

    Answering anonymously — a moderator will review it first.

    Report