Top Strategies for Detecting LLM Hallucination
In this article, we’ll explore general strategies for detecting hallucinations in LLMs (in RAG-based and non-RAG apps).
The latest from our team on Enterprise Generative AI.
Top Strategies for Detecting LLM Hallucination
In this article, we’ll explore general strategies for detecting hallucinations in LLMs (in RAG-based and non-RAG apps).
An Expert’s Guide to Picking Your LLM Tech Stack
Join us as we examine the key layers of an LLM tech stack and help identify the best tools for your needs.
Announcing AIMon’s Instruction Adherence Evaluation for Large Language Models (LLMs)
Evaluation methods for whether an LLM follows a set of verifiable instructions.
How to Fix Hallucinations in RAG LLM Apps
AI hallucinations are real, and fixing them in RAG-based apps is crucial for keeping outputs accurate and useful.
Hallucination Fails: When AI Makes Up Its Mind and Businesses Pay the Price
Stories where AI inaccuracies negatively impacted the operational landscape of businesses.
The Case for Continuous Monitoring of Generative AI Models
Read on to learn about why Generative AI requires a new continuous monitoring stack, what the market offers currently, and what we are building
From Wordy to Worthy: Increasing Textual Precision in LLMs
Detectors to check for completeness and conciseness of LLM outputs.
Introducing Aimon Rely: Reducing Hallucinations in LLM Applications Without Breaking the Bank
Aimon Rely is a state-of-the-art, multi-model system for detecting LLM quality issues like hallucinations offline and online at low cost.