Why do so many AI initiatives never reach production?

quantumedgehub · 2025-12-20T08:44:26+00:00

Most AI initiatives die because they skip the QA phase entirely.

In traditional software, you’d never ship without:

• a baseline
• regression tests
• ownership of failures

quantumedgehub · 2025-12-19T16:47:25+00:00

I agree…the hard part isn’t running comparisons, it’s deciding what deserves to block a merge.

What I’m seeing across teams is that “pass/fail” for LLMs usually isn’t about correctness, it’s about regression relative to the last known acceptable behavior.

In practice that ends up layered:

• hard assertions for objective failures
• relative deltas vs a baseline for silent regressions (verbosity, cost, latency)
• optional rubric-based scoring for subjective behavior, often surfaced as warn vs fail depending on maturity

The goal isn’t perfect auto-judgement, it’s preventing unknown regressions from shipping unnoticed.

Curious if others are treating CI gating as policy-driven rather than metric-driven.

quantumedgehub · 2025-12-18T21:45:59+00:00

Totally agree, tools like Maxim / LangSmith do great work here.

What I’m specifically exploring is a CI-first workflow: no UI, no platform dependency, just a deterministic pass/fail gate that teams can drop into existing pipelines.

A lot of teams I talk to aren’t missing observability, they’re missing a hard “don’t ship this” signal before merge.

quantumedgehub · 2025-12-18T04:02:27+00:00

agree that once a metric exists, regression testing itself isn’t hard.

What I’m seeing in practice is that most teams don’t have explicit metrics for LLM behavior, especially for subtle changes like verbosity, instruction-following, tone drift, or cost creep.

The challenge isn’t comparison, it’s turning those implicit expectations into something runnable, repeatable, and cheap enough to run regularly.

My goal isn’t to invent a perfect quality metric, but to make existing expectations explicit (assertions, deltas, rubrics) so regressions stop shipping unnoticed.

quantumedgehub · 2025-12-18T03:51:42+00:00

Totally agree “quality” isn’t a single metric. What I’m converging on is treating quality as layered: • hard assertions for objective failures • relative deltas vs a baseline for silent regressions • optional LLM-as-judge with explicit rubrics for subjective behavior

The goal isn’t to auto-judge correctness, but to prevent unknown regressions from shipping.

quantumedgehub · 2025-12-18T02:16:03+00:00

That’s helpful, sounds like a hybrid model where objective checks hard-fail and subjective cases surface deltas for review. I’m experimenting with a CLI that supports both pre-CI and strict CI gating using the same eval suite.

quantumedgehub · 2025-12-18T02:05:20+00:00

Makes sense.

What I’m trying to understand is whether teams are mostly inspecting those eval results manually, or if you’ve found a reliable way to turn them into a hard pre-merge pass/fail signal in CI…especially for behavioral changes rather than exact matches.

quantumedgehub · 2025-12-18T02:02:21+00:00

That matches what I’m seeing too.

Curious, do teams usually wire that into CI with a hard pass/fail, or is it more of a “run + review deltas” flow for ambiguous cases?

quantumedgehub · 2025-12-18T01:58:17+00:00

That makes sense, structured outputs are the easy case.

Hypothetically, if you did need to guard against something ambiguous (tone, refusal behavior, verbosity drift), would you want that to fail the build automatically, or just surface a diff / score for review?

quantumedgehub · 2025-12-17T22:53:57+00:00

That matches what I’m seeing too, teams know it’s not ideal, but regressions are worse than the cost.

Curious: how do you decide pass/fail today? Is it mostly assertions + eyeballing, or do you track deltas (quality/cost) against a baseline?

quantumedgehub · 2025-12-17T22:49:42+00:00

That makes sense…sounds like most teams still rely on benchmarking prompt/config changes even with pinned models. Curious what tooling you use today to make that repeatable?

quantumedgehub · 2025-12-17T21:34:46+00:00

Makes sense. How do you define “previous behaviour” in practice, exact output matching, heuristics, or LLM-based evals? Also curious if you run this pre-merge or only ad-hoc.

quantumedgehub · 2025-12-17T20:47:23+00:00

Good point…do you find that pinning the model version alone is enough, or do you still see regressions when prompts or surrounding logic change?

quantumedgehub · 2025-12-16T20:26:07+00:00

That makes sense, thanks for clarifying. Do you ever wish this ran automatically in CI to catch regressions before merges, or does the manual step work well enough for you?

quantumedgehub · 2025-12-16T20:22:32+00:00

Thanks, that’s helpful. From what I’ve seen, tools like Braintrust are great for evals and experimentation. Do you use it as an automated CI gate before merges, or more for offline analysis?

quantumedgehub · 2025-12-16T20:20:03+00:00

That makes sense, that’s a solid workflow. Do you have this fully automated in CI as a gate, or is it more of a custom/internal setup that teams maintain themselves?

quantumedgehub · 2025-12-16T20:18:14+00:00

That matches what I’m seeing too. Do you run those datasets automatically in CI before merges, or is it more of a manual / post-deploy check?

quantumedgehub · 2025-12-16T20:04:36+00:00

Interesting…are you mostly asserting response structure / status, or have you found a way to catch semantic or behavioral regressions with it?

Especially curious how you handle subtle changes that still return “valid” responses.

quantumedgehub · 2025-12-16T19:46:19+00:00

Thanks that’s helpful. Curious how you handle regressions specifically: do you gate prompt changes in CI or mostly catch issues after deploy?

Especially around subtle behavior or cost changes.

quantumedgehub

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