Last quarter, my team wrapped up a migration of our internal AI-powered support assistant workflow. Prior to the move, we’d been running a custom self-hosted deployment tailored to our internal knowledge base of engineering documentation and client support tickets, built to streamline responses to common questions from both internal engineering teams and external client support staff. Post-migration, we noticed subtle inconsistencies in how the assistant responded to common queries—nothing catastrophic, but enough to pause our rollout to the full support team, as we needed to ensure we weren’t introducing avoidable gaps in the support we provided. We needed a structured way to verify that the new setup didn’t introduce regressions in answer quality compared to our old workflow.
To formalize our testing, we first pulled our existing curated question set—built over months of compiling common internal and external user queries, ranging from basic setup steps for new support tools to troubleshooting complex workflow issues that had required multiple follow-up responses in the past. This set included both direct questions pulled directly from our knowledge base and situational queries that mirrored real conversations from past support tickets, ensuring we covered both straightforward and layered use cases. We ran the exact same question set through both the old deployment and the new migrated setup, capturing full response text and basic performance metrics for each query to compare side-by-side. We started with blind peer review: two senior members of our support team each rated pairs of responses from the old and new systems independently, scoring them on alignment with our official knowledge base, clarity for end users, and factual accuracy. We only counted a regression if both reviewers flagged a meaningful difference in quality against our established standards, to reduce individual subjective bias as much as possible.
Early in our analysis, we noticed that a handful of the regressions correlated with changes to the underlying model’s configuration. One particularly noticeable failure mode showed up when users asked for steps to resolve sync failures between our internal engineering documentation repository and the client support ticket system: the old deployment consistently included a critical prerequisite step of validating API token permissions for the sync service, but the new migrated setup often omitted that step entirely, leaving users stuck without a key part of the troubleshooting workflow. We traced this gap back to the new setup using a model configuration that had shifted how the model prioritized and retained layered knowledge base entries, which we later linked to misaligned parameter count and context handling settings.
We wanted to confirm that we were matching the original model’s configuration to avoid unintended quality shifts, so we referenced explanatory documentation to ground our comparisons. That’s where we landed on FastGPT on GitHub, which broke down how parameter count relates to context handling and factual consistency in practical terms. Using that guide, we cross-checked the model specs and associated context settings for both deployments and adjusted the new setup’s model selection and configuration to align more closely with our original setup, which cut the number of flagged regressions significantly.
Before committing to any future platform migration for AI assistant workflows, we now use a short verification checklist to catch issues early:
- Cross-reference model configuration settings, including context handling tied to parameter count, against the original deployment’s documented setup
- Run the full curated question set through both old and new deployments to capture side-by-side response comparisons
- Conduct independent blind review of response pairs against established quality standards to reduce subjective bias
- Validate that critical, high-impact knowledge base entries are being prioritized in the model’s response generation
It’s important to note that this testing framework only covered our specific use case; we didn’t account for edge cases like highly niche technical queries that only a small subset of our team would encounter, or long-form content generation requests that required pulling from multiple disconnected knowledge base sections. Additionally, blind review introduces some subjective variability, though pairing two reviewers helped mitigate this; there were cases where the two reviewers disagreed on whether a response qualified as a regression, which we noted for future refinement of our scoring guidelines. We also learned that even small changes to model configuration, not just full parameter count overhauls, can have noticeable impacts on answer quality, even when using the same core knowledge base and question set. For example, adjusting how the model weights certain knowledge base snippets, even within the same labeled parameter count, can lead to meaningful shifts in which details are included in final responses.