All New: Evaluations for RAG & Chain applications
Unlock the potential of RAG analysis with 4 essential metrics to enhance performance and decision-making. Learn how to master RAG methodology for greater effectiveness in project management and strategic planning.
Learn about how to identify and detect LLM hallucinations
Join in on this workshop where we will showcase some powerful metrics to evaluate the quality of the inputs (data quality, RAG context quality, etc) and outputs (hallucinations) with a focus on both RAG and fine-tuning use cases.
Webinar - Announcing Galileo LLM Studio: A Smarter Way to Build LLM Applications
A survey of hallucination detection techniques
Learn advanced chunking techniques tailored for Language Model (LLM) applications with our guide on Mastering RAG. Elevate your projects by mastering efficient chunking methods to enhance information processing and generation capabilities.
The Hallucination Index provides a comprehensive evaluation of 11 leading LLMs' propensity to hallucinate during common generative AI tasks.
Dive into our blog for advanced strategies like ThoT, CoN, and CoVe to minimize hallucinations in RAG applications. Explore emotional prompts and ExpertPrompting to enhance LLM performance. Stay ahead in the dynamic RAG landscape with reliable insights for precise language models. Read now for a deep dive into refining LLMs.
Learn about different types of LLM evaluation metrics needed for generative applications
Learn how to Master RAG. Delve deep into 8 scenarios that are essential for testing before going to production.
A comprehensive guide to retrieval-augmented generation (RAG), fine-tuning, and their combined strategies in Large Language Models (LLMs).
ChainPoll: A High Efficacy Method for LLM Hallucination Detection. ChainPoll leverages Chaining and Polling or Ensembling to help teams better detect LLM hallucinations. Read more at rungalileo.io/blog/chainpoll.
Explore the transformative impact of President Biden's Executive Order on AI, focusing on safety, privacy, and innovation. Discover key takeaways, including the need for robust Red-teaming processes, transparent safety test sharing, and privacy-preserving techniques.
Watch our webinar with Pinecone on optimizing RAG & chain-based GenAI! Learn strategies to combat hallucinations, leverage vector databases, and enhance RAG analytics for efficient debugging.
The creation of human-like text with Natural Language Generation (NLG) has improved recently because of advancements in Transformer-based language models. This has made the text produced by NLG helpful for creating summaries, generating dialogue, or transforming data into text. However, there is a problem: these deep learning systems sometimes make up or "hallucinate" text that was not intended, which can lead to worse performance and disappoint users in real-world situations.
Working with Natural Language Processing?
Read about Galileo’s NLP Studio