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You spent six weeks learning your AI pipeline is biased. It is vulnerable. It is hard to audit. Some fixes work. These fixes are peer reviewed. You ship them this week.
Use a generator and a judge from different model families. Use OpenAI for generation. Use Anthropic for judging. This stops self preference bias. Models stop liking their own style.
Stop asking if a response is good. Ask for scores on these points:
- Accuracy
- Completeness
- Tone
- Actionability This reduces bias by 31.5 percent.
Force the judge to reason before it scores. Make it list facts. Make it check each fact. Then it assigns a score. This adds 1.5 to 13 accuracy points.
Do not monitor single outputs. Watch the whole population. Look for score distribution shifts. This catches drift and attacks early.
Avoid competitive setups. Agents should not argue. Use cooperative setups. One agent generates. One agent finds gaps. One agent fills gaps. This improves robustness by 68 percent.
Your Checklist:
This Week:
- Add reasoning to prompts.
- Use structured evaluation.
- Check your model families.
This Month:
- Set up cross family evaluation.
- Start population monitoring.
This Quarter:
- Test for adversarial attacks.
- Move to cooperative design.
You will not solve this completely. You will reduce bias. You will catch errors faster. This is the goal.
Source: https://dev.to/sayokbose91/part-6-of-6-how-to-build-pipelines-that-dont-gaslight-themselves-dci Optional learning community: https://t.me/GyaanSetuAi