2. Adjust Feedback Threshold

In the following you will learn:

1. What a feedback similarity threshold is.

2. Why feedback has less effect after a while.

3. Which is why you have to increase the threshold to ensure the quality of the response.

💡The closer the value is to 1, the more likely the feedback given will be taken into account in a similar search query. Recommendation: 0.85 - 0.95.

To adjust the feedback threshold, navigate to Algorithm — > AI Settings

1) How does the Feedback Similarity Threshold work?

• When the AI's response is evaluated or corrected, the correct feedback is stored.

• In the future, the AI will check: "Is the new user question semantically similar enough to the feedback?"

• This similarity is controlled by a threshold :

  • Low threshold → Even rough similarities are enough, feedback is often applied.

  • High threshold → Only very close (semantically "dense") hits are used.

2. Why does feedback no longer work after some time?

In the beginning:

• Users give a lot of new feedback → even with a low threshold there is a clear learning effect.

• Every correction noticeably improves the answers.

After some time:

• The system has already learned many similar cases.

• With a low threshold, it matches feedback even to vaguely similar questions.

• The result: The feedback is applied too broadly → it is watered down, it does not appear precise, and the quality stagnates.

3. Why does the threshold need to be increased?

• As the amount of feedback increases, the "density" in the embedding memory grows.

• In order to use relevant and really appropriate feedback, you have to tighten the threshold.

• As a result, the AI ignores inaccurate feedback that does not fit 1:1.

• This prevents quality from stagnating or even deteriorating due to incorrect overuse of feedback.

4. Analogy to the explanation

• Imagine sorting screws into boxes by size:

• Initially, large categories (small, medium, large) are sufficient. → It helps immediately.

• Later, when you have many different screws, this rough division is no longer enough.

• You need finer categories (10 mm, 12 mm, 14 mm ...).

• This is exactly what the higher similarity threshold does: it ensures more precise fits so that the feedback is effective.

5. Conclusion

• In the beginning: low threshold → feedback seems broad, fast learning curve.

• Over time: more feedback data → low threshold dilutes → quality stagnates.

• Solution: Increase the threshold so that only really suitable feedback takes effect → the answers become more precise again.