Your RAG pipeline doesn't tell you when it's wrong. Here's how to fix that.

1. 1Add a numeric trust score to LLM output using Python
2. 2Implement a 3-line code solution to calculate trust scores
3. 3Integrate trust scores into your existing RAG pipeline to improve accuracy

Data scientists and machine learning engineers can benefit from this technique to increase the trustworthiness of their LLM models

💡 Adding a trust score to LLM output can significantly improve model reliability and accuracy

🚀 Boost your LLM's reliability with a simple trust score! 💡