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Top Open And Closed Source LLMs For Short, Medium and Long Context RAG
An exploration of type of hallucinations in multimodal models and ways to mitigate them.
A survey of hallucination detection techniques
Learn to do robust evaluation and beat the current SoTA approaches
Galileo on Google Cloud accelerates evaluating and observing generative AI applications.
Research backed evaluation foundation models for enterprise scale
Master the art of selecting vector database based on various factors
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 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
Understand the most common issues with AI agents in production.
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.
A step-by-step guide for evaluating smart agents
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.
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
A comprehensive guide to retrieval-augmented generation (RAG), fine-tuning, and their combined strategies in Large Language Models (LLMs).
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!
We built this leaderboard to answer one simple question: "How do AI agents perform in real-world agentic scenarios?"
A comprehensive guide to metrics for GenAI chatbot agents
Effective human assistance in AI agents
Top research benchmarks for evaluating agent performance for planning, tool calling and persuasion.
Unlock the potential of LLM Judges with fundamental techniques
Learn to bridge the gap between AI capabilities and business outcomes
Industry report on how generative AI is transforming the world.
The Hallucination Index provides a comprehensive evaluation of 11 leading LLMs' propensity to hallucinate during common generative AI tasks.
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.
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.
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.
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.
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!
Learn to setup a robust observability solution for RAG in production
Learn to create and filter synthetic data with ChainPoll for building evaluation and training dataset
Llama 3 insights from the leaderboards and experts
Galileo's key takeaway's from the 2023 Open AI Dev Day, covering new product releases, upgrades, pricing changes and many more!
Learn about how to identify and detect LLM hallucinations
While many teams have been building LLM applications for over a year now, there is still much to learn about RAG and all types of hallucinations. Check out our roundup of the top generative AI and LLM articles for August 2024.
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.
A technique to reduce hallucinations drastically in RAG with self reflection and finetuning
Select the best framework for building intelligent AI Agents
Learn the intricacies of evaluating LLMs for RAG - Datasets, Metrics & Benchmarks
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