All New: Evaluations for RAG & Chain applications
Master the art of selecting vector database based on various factors
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.
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.
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.
Working with Natural Language Processing?
Read about Galileo’s NLP Studio