Long threads quietly change the problem
I once spent three days inside a single chat thread while building a small service. The first few turns pinned the language, the runtime, and a handful of constraints. By the end of the third day the model was still giving answers that read fine but had slipped into recommending patterns from a different ecosystem. The output started using typed return values and import names that only exist in a more recent framework. It was subtle; the code compiled locally with tweaks, but it violated the constraint we had set at the beginning: no new runtime features.
That drift feels like a memory leak. Attention focuses on recent tokens, so the earliest constraints fade even though the full conversation is technically in context. I stopped trusting long living threads after that. Resetting the thread, or repeating essential constraints every few hundred messages, forced the model back to the right assumptions.
Hidden assumptions stack up
What the model doesn’t say out loud are the defaults it silently picked. I asked for SQL migrations and the model generated column defaults assuming UTC. The dev and the staging DBs used local time. Tests passed because test fixtures mocked dates, then production shipped wrong event times. That one mistake was avoidable but invisible in the chat: the model assumed a particular timezone and ignored our schema conventions.
Other hidden assumptions I’ve seen include library versions, default serializers, and even whether a function is pure. Once, it returned examples that used a global config flag I never introduced. When that flag never existed in our codebase, the integration code simply no-oped. These are not dramatic hallucinations. They are small, plausible defaults that silently break later stages.
Tool failures masquerade as reasoning
We hooked a test-execution tool to the assistant. It times out intermittently. A few times the assistant received a partial test log and then continued to explain why the test suite passed. The explanation was convincing. It was wrong. The tool had errored, not the tests. The assistant filled the gap with the most probable narrative.
That taught us to treat tool responses as untrusted first-class inputs. We now log the raw tool response, its status code, and a checksum before the assistant sees it. If the tool returns truncated output we abort the assistant step and surface the failure to a human. That extra wiring added complexity but prevented several incidents where an assistant invented the rest of a failing trace.
Small mistakes compound along pipelines
I’ve watched this cascade: the assistant suggests a compact parser, we adapt it, unit tests run against a mocked input that never captures edge cases, CI goes green, and production sees malformed payloads. One tiny mismatch in how we handled optional fields multiplied into five different error branches. Each stage accepted the previous artifact as correct because it matched the most recent proximate checks.
In practice those pipelines reward fluency over correctness. When you build a sequence of dependent steps you also build a chain where each link can silently normalize a mistake into accepted behavior.
Guardrails that actually helped
We changed three things that reduced surprise. First, we shortened conversational spans: start fresh threads for distinct tasks and use a strict system message that lists non-negotiable constraints. Second, we automated verification: compile and run focused unit tests and static analyzers on every assistant-suggested change before it hits review. Third, we instrument tool outputs. Every tool call is logged and validated; timeouts or partial outputs trigger explicit error paths.
I also compare answers from multiple models in a shared workspace when I need confidence. Seeing disagreement quickly exposes hidden assumptions. That workflow lives in a chat-based comparison setup we use internally and in a few experiments with a shared multi-model workspace like the multi-model chat. For anything time sensitive or source dependent I force a sourcing pass tied into external documents and issue trackers and a structured verification step inspired by research workflows described at deep research. None of this is perfect. I still slip up. But instrumenting failures and insisting on cheap, automatic checks caught the mistakes that used to surprise me most.
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