We actually found context rot by relying on the knowledge source. We've left it as is but copied/moved much of it to the prompt. I'm not going to post my prompt here but there's constant work to be done on it.
Everytime the AI decides to do something new we have to go back in and readjust.
I will spend another couple of weeks fine tuning and going over your KIND response.
Dear guzzitech,
I think your approach is going to lead you to a painful experience.
If you keep moving more and more knowledge into the prompt, you are effectively forcing the model to carry too much information in its runtime context. The system prompt is best used for behavior, rules, tone, and process - not as a large knowledge base.
Once the prompt becomes too large, you can start seeing issues such as:
- Less consistent behavior
- Changes in tone
- Forgetting important instructions
- Random or unexpected responses
- The model prioritizing the wrong information
This is usually a sign that the prompt is becoming too heavy.
A better approach is to keep the prompt short and structured, then use the knowledge source for reference material. The prompt should tell the AI how to behave and how to use the knowledge, not contain all the knowledge itself.
You can check your prompt size here:
https://platform.openai.com/tokenizer
As a rough guide, if the prompt is getting close to 4,000 tokens or more, I would start reviewing and simplifying it. Even if the model supports a much larger context window, filling it with too much static information can still reduce consistency.
So my recommendation would be:
Keep the system prompt lean.
Move business knowledge into the knowledge source.
Use the prompt only for rules, call flow, tone, and escalation logic.
You can also send the prompt as a Private message and I can skim through it to at least give you some pointers.
These videos may also help explain the issue:
God bless