HP + Galileo Partner to Accelerate Trustworthy AI
Galileo LLM Studio enables Pineonce users to identify and visualize the right context to add powered by evaluation metrics such as the hallucination score, so you can power your LLM apps with the right context while engineering your prompts, or for your LLMs in production
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.
Galileo x Zilliz: The Power of Vector Embeddings
A survey of hallucination detection techniques
Galileo's key takeaway's from the 2023 Open AI Dev Day, covering new product releases, upgrades, pricing changes and many more!
Research backed evaluation foundation models for enterprise scale
Galileo on Google Cloud accelerates evaluating and observing generative AI applications.
Learn to setup a robust observability solution for RAG in production
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.
Explore the nuances of crafting an Enterprise RAG System in our blog, "Mastering RAG: Architecting Success." We break down key components to provide users with a solid starting point, fostering clarity and understanding among RAG builders.
Learn how to Master RAG. Delve deep into 8 scenarios that are essential for testing before going to production.
Learn about different types of LLM evaluation metrics needed for generative applications
Smaller LLMs can be better (if they have a good education), but if you’re trying to build AGI you better go big on infrastructure! Check out our roundup of the top generative AI and LLM articles for April 2024.
Master the art of selecting vector database based on various factors
Stay ahead of the AI curve! Our February roundup covers: Air Canada's AI woes, RAG failures, climate tech & AI, fine-tuning LLMs, and synthetic data generation. Don't miss out!
The AI landscape is exploding in size, with some early winners emerging, but RAG reigns supreme for enterprise LLM systems. Check out our roundup of the top generative AI and LLM articles for May 2024.
LLM Studio helps you develop and evaluate LLM apps in hours instead of days.
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.
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.
February's AI roundup: Pinterest's ML evolution, NeurIPS 2023 insights, understanding LLM self-attention, cost-effective multi-model alternatives, essential LLM courses, and a safety-focused open dataset catalog. Stay informed in the world of Gen AI!
A comprehensive guide to retrieval-augmented generation (RAG), fine-tuning, and their combined strategies in Large Language Models (LLMs).
Llama 3 insights from the leaderboards and experts
An exploration of type of hallucinations in multimodal models and ways to mitigate them.
Learn to do robust evaluation and beat the current SoTA approaches
Working with Natural Language Processing?
Read about Galileo’s NLP Studio