safe.llm
Research and guides for building safe and reliable AI products. Helping thousands of AI engineers build safer products.
From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
The EVER (Real-Time Verification and Rectification) framework is designed to dynamically mitigate hallucinations during text generation by ensuring the accuracy and trustworthiness of each sentence before proceeding.
How to evaluate your Llama Index query engine using Ragas evals + Athina AI
If you're using Llama Index to work with advanced retrieval strategies, you're going to need a great evaluation setup. Here's how you can use Athina's SDK to run Ragas evals on your Llama Index RAG pipeline.
Safety
Evaluation
Prompt Engineering
Hallucinations
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
The EVER (Real-Time Verification and Rectification) framework is designed to dynamically mitigate hallucinations during text generation by ensuring the accuracy and trustworthiness of each sentence before proceeding.
From Noise to Clarity: Unraveling the Adversarial Suffix of Large Language Model Attacks via Translation of Text Embeddings
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
The EVER (Real-Time Verification and Rectification) framework is designed to dynamically mitigate hallucinations during text generation by ensuring the accuracy and trustworthiness of each sentence before proceeding.