Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
/ quick answer
A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates. Continuous sampling and evals caught silent degradation after a model update.
/ frequently asked
What is the Platform Catches a 19% Quality Drop Before Users Did use case?
A document-processing platform ran extraction for 300 business customers with no output monitoring beyond error rates.
What was the outcome?
After adding a 45-case eval suite and daily sampling, a provider-side model change showed up as a 19% accuracy drop within 36 hours. The team pinned the previous version, fixed the prompt and shipped with no customer-visible incident.
Which tools were used?
ai-observability-stack, ai-testing-stack.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Monitoring
AI monitoring is production observability for model-driven systems: traces, cost, latency, tool failures and output-quality drift.
- →AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
- →Automation Observability
Monitoring inputs, model calls, outputs, cost, latency, and failures across AI workflows.
- →Evals
Automated tests that grade LLM outputs against expected behavior.
Related workflows
Turn this into a repeatable process.
- →Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
- →Monitor an AI System in Production
See quality, cost and failure drift before your users report it.
- →How to Build and Launch a SaaS With Vibe Coding
From spec to paying customers: auth, database, payments and launch with an AI builder.
- →Competitor Price Monitoring
Track competitor pricing pages daily and alert on changes.
Related tool stacks
The tools that run it in production.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
- →Agentic AI Ops Monitoring Stack
This stack uses an AI browser, an LLM, and an orchestration layer to autonomously monitor IT systems, identify anomalies from web-based dashboards, and generate actionable insights for operations teams. It enhances proactive problem resolution.
- →Indie SaaS Launch Stack
Everything a solo founder needs to ship and monetize a SaaS in weeks.
Related prompts
Reusable prompts for this job.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
Related use cases
How people apply it, and what came out.
- →Indie Hacker Ships a Micro-SaaS in 9 Days
A solo dev took an idea to paying users in under two weeks using an AI-first stack.
- →Indie Hacker Launches Paid SaaS in 30 Days
One founder, one weekend of validation, one month to $2k MRR — with an AI-native stack.
- →SaaS Support Team Automates Tier-1, Focuses on Retention
Support org deflects 55% of tickets and re-invests the time into proactive retention.
- →SaaS Cuts First-Response Time from 6h to 4min with an Agent
A 12-person SaaS shipped a tier-1 support agent and kept humans on exceptions only.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.