Context windows are not stable
I kept treating the model like a stateful engineer who remembered every constraint I set at the kickoff. That was a mistake. In a 10-hour debugging thread the model slowly abandoned the original constraints: the project required Python 3.8 and synchronous code, but after a dozen turns it started returning async-native snippets and typing features only valid in later versions. The behavior is not random. Attention favors recent tokens. Early constraints still live in the context, but they become low-signal compared with the latest clarifications, code samples, and error dumps.
Practically that meant I merged a suggested fix that introduced an await where none of our call sites used async. Tests failed in CI. It is easy to blame the model, but the real issue was my workflow: I let a long thread accrete details without reasserting the hard constraints that matter for correctness.
Small drift cascades into big bugs
One pull request started as a handful of harmless changes. The model recommended a shorter SQL query, then a schema tweak, then a field rename to match the query. Each suggestion looked plausible in isolation. I accepted three of them. Everything broke in production when a client relied on a stable column name and the new query had different ordering semantics. The sequence of small edits created a brittle chain of assumptions.
Drift is subtle because it often preserves local consistency. The model rewrites around its current context. Unit tests can still pass if they cover the new local logic. Integration tests and downstream consumers pick up the real damage later. In my case a nightly job silently started producing incorrect aggregates for a week before anyone noticed.
Hidden assumptions live between the lines
Most failure modes came from implicit defaults the model quietly used. Timezones, SQL dialects, pagination behavior, and API versioning kept tripping us. For example, the model emitted a SQL LIMIT without ORDER BY and the rows returned from our replica cluster were non-deterministic. It looked like a logic error in our aggregation code but the underlying cause was a missing ORDER BY that the model assumed was obvious.
Tool integration makes this worse. A failed tooling call returns partial or empty data, the model fills in the gap with plausible content, and that content becomes code. I’ve seen query outputs truncated by a timeout, then the model inventing a schema to continue. From the outside that reads as hallucination. Under the hood it was a missing guardrail on the tool response.
Practical ways I catch drift: logging, snapshots, and checks
After enough quiet failures I started treating every model turn as auditable. I snapshot prompts and responses alongside a short context hash. I log the last few system and user messages that influenced a suggestion. That lets me answer the question: which tokens were recent enough to sway the output? When a suggestion causes a test failure I replay the exact prompt and confirm the drift.
I also require machine-checks before human review. Generated JSON or schema-shaped outputs get validated with strict parsers. Generated SQL runs against a read-only sandbox. If the model calls external tools I assert response shapes and non-empty payloads before accepting downstream changes. For broader verification I keep experiments in a shared chat workspace so I can compare answers across models and sessions, and when I need a focused chain of sourcing I push notes into a research flow for cross-checking with a focused sourcing tool.
Operational guardrails that stopped surprises
Concrete measures worked better than vague rules. I started resetting conversations at logical boundaries, not just when I felt like it. For a single feature I keep a compact prompt that lists non-negotiable constraints and include it at the top of every turn. I enforce schema validators on model outputs and fail fast when a generated patch changes public identifiers. That saved us from two accidental API contract changes.
We also added a cheap human-in-the-loop gate for changes that touch migrations or downstream contracts. No model suggestion is merged until CI runs a set of focused integration checks. I use a shared environment to compare alternative completions because disagreement is informative in a way single answers are not. When a tool call seems flaky I record the failure and treat the model output as untrusted until the tooling error is resolved.
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