Galileo secures $45M in Series B funding to boost its Evaluation Intelligence Platform, driving AI accuracy and trust for enterprise teams. With backing from leading investors, Galileo is transforming how companies like HP and Twilio evaluate AI performance.
Join us at AWS re:Invent to see the latest in AI evaluation intelligence and learn from leading GenAI experts!
Unlock the potential of LLM Judges with fundamental techniques
Master the art of building your AI evaluators using LLMs
Galileo’s native integrations with Databricks makes it simple for enterprises to use Databricks models for LLM evaluation and programmatically build training and evaluation datasets for model improvement.
Understand the tradeoffs between LLMs and humans for generative AI evaluation
Industry report on how generative AI is transforming the world.
Explore insights from industry leaders on the evolving GenAI stack at Galileo's GenAI Productionize conference. Learn how enterprises are adopting LLMOps, optimizing costs, fine-tuning models, and improving data quality to harness the power of generative AI. Discover key trends and strategies for integrating GenAI into your organization.
Win prizes while driving the future roadmap of GenAI Studio. Sign up now!
Learn how Clearwater Analytics, the leading SaaS-based investment accounting and analytics solution, built and deployed a customer-facing, multi-agent system using fine-tuned SLMs.
Learn to create and filter synthetic data with ChainPoll for building evaluation and training dataset
See how easy it is to leverage Galileo's platform alongside the IBM watsonx SDK to measure RAG performance, detect hallucinations, and quickly iterate through numerous prompts and LLMs.
Learn the intricacies of evaluating LLMs for RAG - Datasets, Metrics & Benchmarks
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.
Learn how to build scalable, reliable, highly personalized agentic solutions, including best practices for bringing agentic solutions to production.
Top Open And Closed Source LLMs For Short, Medium and Long Context RAG
The LLM Hallucination Index ranks 22 of the leading models based on their performance in real-world scenarios. We hope this index helps AI builders make informed decisions about which LLM is best suited for their particular use case and need.
Galileo and HP partner to enable faster and safer deployment of AI-powered applications.
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
Research backed evaluation foundation models for enterprise scale
Low latency, low cost, high accuracy GenAI evaluation is finally here. No more ask-GPT and painstaking vibe checks.
Evaluations are critical for enterprise GenAI development and deployment. Despite this, many teams still rely on 'vibe checks' and manual human evaluation. To productionize trustworthy AI, teams need to rethink how they evaluate their solutions.
Join us at Databricks Data+AI Summit to see the latest innovations at the convergence of data and AI and learn from leading GenAI experts!
We’re excited to announce Galileo Protect – an advanced GenAI firewall that intercepts hallucinations, prompt attacks, security threats, and more in real-time! Register for our upcoming webinar to see Protect live in action.
We're thrilled to unveil Galileo Protect, an advanced GenAI firewall solution that intercepts hallucinations, prompt attacks, security threats, and more in real-time.
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.
It’s time to put the science back in data science! Craig Wiley, Sr Dir of AI at Databricks, joined us at GenAI Productionize 2024 to share practical tips and frameworks for evaluating and improving generative AI. Read key takeaways from his session.
Llama 3 insights from the leaderboards and experts
At GenAI Productionize 2024, expert practitioners shared their own experiences and mistakes to offer tools and techniques for deploying GenAI at enterprise scale. Read key takeaways from the session on how to productionize generative AI.
2024 has been a landmark year for generative AI, with enterprises going from experimental proofs of concept to production use cases. At GenAI Productionize 2024, our enterprise executive panel shared lessons learned along their AI adoption journeys.
Learn to setup a robust observability solution for RAG in production
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.
A technique to reduce hallucinations drastically in RAG with self reflection and finetuning
Join Ya Xu, Head of Data and AI at LinkedIn, to learn the technologies, frameworks, and organizational strategies she uses to scale GenAI at LinkedIn.
Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.
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!
Unsure of which embedding model to choose for your Retrieval-Augmented Generation (RAG) system? This blog post dives into the various options available, helping you select the best fit for your specific needs and maximize RAG performance.
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.
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.
Introducing a powerful set of workflows and research-backed evaluation metrics to evaluate and optimize RAG systems.
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!
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.
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.
Galileo on Google Cloud accelerates evaluating and observing generative AI applications.
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.
Prepare for the impact of the EU AI Act with our actionable guide. Explore risk categories, conformity assessments, and consequences of non-compliance. Learn practical steps and leverage Galileo's tools for AI compliance. Ensure your systems align with regulatory standards.
Learn how to Master RAG. Delve deep into 8 scenarios that are essential for testing before going to production.
The Hallucination Index provides a comprehensive evaluation of 11 leading LLMs' propensity to hallucinate during common generative AI tasks.
Galileo's key takeaway's from the 2023 Open AI Dev Day, covering new product releases, upgrades, pricing changes and many more!
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.
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.
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 comprehensive guide to retrieval-augmented generation (RAG), fine-tuning, and their combined strategies in Large Language Models (LLMs).
Webinar - Announcing Galileo LLM Studio: A Smarter Way to Build LLM Applications
Learn about how to identify and detect LLM hallucinations
LLM Studio helps you develop and evaluate LLM apps in hours instead of days.
Learn about different types of LLM evaluation metrics needed for generative applications
A survey of hallucination detection techniques
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.
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
The Data Error Potential (DEP) is a 0 to 1 score that provides a tool to very quickly sort and bubble up data that is most difficult and worthwhile to explore when digging into your model’s errors. Since DEP is task agnostic, it provides a strong metric to guide exploration of model failure modes.
Galileo integrates deeply with Label Studio to help data scientists debug and fix their training data 10x faster.
Using Galileo you can surface labeling errors and model errors on the most popular dataset in computer vision. Explore the various error type and simple visualization tools to find troublesome data points.
Unpack the findings of our State of Machine Learning Data Quality Report. We have surveyed 500 experienced data professionals to learn what types of data they work with, what data errors they encounter, and what technologies they use.
Learn how to instantly resolve data errors using Galileo. Galileo Machine Learning Data Quality Intelligence enables ML Practitioners to resolve data errors.
HuggingFace has proved to be one of the leading hubs for NLP-based models and datasets powering so many applications today. But in the case of NER, as with any other NLP task, the quality of your data can impact how well (or poorly) your models perform during training and post-production.
One neglected aspect of building high-quality models is that it depends on one crucial entity: high quality data. Good quality data in ML is the most significant impediment to seamless ML adoption across the enterprise.
Putting a high-quality Machine Learning (ML) model into production can take weeks, months, or even quarters. Learn how ML teams are now working to solve these bottlenecks.
When working on machine learning (ML) projects, the challenges are usually centered around datasets in both the pre-training and post-training phases, with their own respective issues that need to be addressed. Learn about different ML data blind spots and how to address these issues.
At Galileo, we had a simple goal: enable machine learning engineers to easily and quickly surface critical issues in their datasets. This data-centric approach to model development made sense to us but came with a unique challenge that other model-centric ML tools did not face: data is big. Really big. While other tracking tools were logging *meta-*data information such as hyper-parameters, weights, and biases, we were logging embedding and probability vectors, sometimes in the near thousand-dimension space, and sometimes many per input.
Machine Learning is advancing quickly but what is changing? Learn what the state of ML is today, what being data-centric means, and what the future of ML is turning into.
In this article, Galileo founding engineer Nikita Demir discusses common data errors that NLP teams run into, and how Galileo helps fix these errors in minutes, with a few lines of code.
Build better models, faster, with better data. We will dive into what is ML data intelligence, and it's 5 principles you can use today.
Data is critical for ML. But it wasn't always this way. Learn about how focusing on ML Data quality came to become the central figure for the best ML teams today.
We used Galileo on the popular MIT dataset with a NER task, to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available for use.
In this post, we discuss the Named Entity Recognition (NER) task, why it is an important component of various NLP pipelines, and why it is particularly challenging to improve NER models.
We used Galileo on the popular Newsgroups dataset to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available publicly for use.
“The data I work with is always clean, error free, with no hidden biases” said no one that has ever worked on training and productionizing ML models. Learn what ML data Intelligence is and how Galileo can help with your unstructured data.