๐ง๐ฒ๐๐๐ถ๐ป๐ด ๐ก๐ผ๐ป-๐๐ฒ๐๐ฒ๐ฟ๐บ๐ถ๐ป๐ถ๐๐๐ถ๐ฐ ๐๐ ๐๐ด๐ฒ๐ป๐๐
AI incidents rose from 233 to 362 in one year. Hallucination rates hit 94% in some models. AI quality is now a bottleneck.
Traditional QA fails for AI. Old QA expects one fixed output for one input. AI Agents interpret intent. They use tools and context. They change their path based on conditions.
You need a new framework for non-deterministic systems.
Start with these three basics:
- Tracing: Record all prompts and tool calls.
- Versioning: Track your prompts and models.
- Environment: Make tests repeatable for everyone.
Use this 5-layer testing framework:
- Layer 1: Unit Testing. Test small parts. Use a golden dataset of 50 to 200 examples.
- Layer 2: Trajectory. Check the reasoning path. Stop infinite loops and redundant tool calls.
- Layer 3: Task. Check if the user goal is met. Use AI simulators to act as users.
- Layer 4: Safety. Run adversarial tests. Scan for leaked private data.
- Layer 5: Production. Use shadow environments. Track real user feedback.
Avoid these traps:
- Thinking temperature zero stops randomness. Hardware still causes variance.
- Using AI judges for numeric scores. AI is bad at subtle number differences.
- Testing parts in isolation. Errors multiply across a full session.
AI is not stable. Continuous evaluation is the only way. This protects your data and your users.
Source: https://dev.to/ella-wilson/a-practical-framework-for-testing-non-deterministic-ai-agents-4hk0
Optional learning community: https://t.me/GyaanSetuAi