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Discover how Galileo and NVIDIA NeMo microservices created a powerful data flywheel that enabled Cisco to deploy reliable AI agents with 10x lower latency metrics and 40% higher accuracy for business-critical operations.
Are you finding it challenging to monitor and optimize your large language models effectively? As AI applications become more complex and integral to business operations, understanding LLM observability is crucial. Understanding LLM observability can help you enhance the performance and reliability of your AI applications, especially after deployment.
Discover how AI is creating information symmetry in enterprises by breaking down silos, democratizing data access, and transforming organizational decision-making.
Learn how to create your own intelligent, multi-API AI agent using the Agent Connect Protocol (ACP). This tutorial walks you through building a weather-predicting, fashion-recommending, mood-setting assistant with real-time evaluation powered by Galileo.
Discover what Agentic AI can actually deliver for your business. Learn to identify valuable AI solutions, avoid market saturation pitfalls, and implement AI that solves real problems instead of chasing trends.
Learn how Specification-First AI Development aligns technical capabilities with business goals. Reduce errors and improve outcomes with detailed specifications.
Discover strategies to adapt Test-Driven Development for AI, ensuring reliability in non-deterministic outputs. Tackle AI testing challenges with innovative methods.
Explore 9 essential strategies to maintain stability in dynamic multi-agent systems. Discover adaptive architectures, robust communication, & monitoring strategies.
Explore the differences of collaborative and competitive multi-agent systems in AI. Discover interaction paradigms for agent dynamics in diverse scenarios.
Master AI Governance with these 7 strategies. Learn how to minimize risks, accelerate innovation, and ensure compliance with industry standards.
Discover best practices for evaluating AI agents, from accuracy metrics and action evaluations to real-time monitoring, to ensure reliable AI-driven automation.
Discover the importance of AI observability for reliable systems. Learn how monitoring, debugging, and transparency enhance AI performance and trust.
Learn how to model and mitigate systemic risk in multi-agent AI systems. Discover failure cascade simulations, layered threat analysis, and real-time monitoring strategies with Galileo.
Uncover the essentials of CI for AI. Learn how to automate, adapt, and optimize workflows, ensuring robust AI models & seamless development.
Discover how to measure communication efficiency in multi-agent AI systems. Learn practical strategies, metrics, and optimizations to boost performance.
Join Galileo and Cisco to explore the infrastructure needed to build reliable, interoperable multi-agent systems, including an open, standardized framework for agent-to-agent collaboration.
Explore strategies to identify and counteract coordinated threats in multi-agent AI. Protect against exploitation through communication and trust safeguards.
Learn strategies to detect and prevent malicious behavior in multi-agent systems. Explore security challenges, detection frameworks, and prevention strategies.
Explore centralized vs. distributed AI strategies in multi-agent systems. Learn the impact on decision-making, scalability, and fault tolerance.
Learn effective strategies to prevent data corruption in multi-agent AI workflows. Enhance reliability and secure sensitive data across complex AI systems.
Explore top strategies for Large Language Model (LLM) summarization. Learn to implement tech solutions and optimize document processing efficiency.
Explore innovative cross-validation techniques for enhancing LLM performance. Boost generalization & reliability with tailored validation strategies.
Explore how Large Language Model Reasoning Graphs enhance recommender systems, addressing traditional challenges with interpretable, context-aware recommendations.
Discover MoverScore, a semantic evaluation metric that outperforms BLEU and ROUGE in capturing text nuances, offering a refined approach to AI-generated content quality assessment.
Explore a detailed step-by-step guide on effectively evaluating AI systems to boost their potential. Understand risks, optimize performance, and ensure compliance.
Explore strategies for building trust and transparency in enterprise AI, including regulated industries. Build safer AI.
Unlock AI potential with our step-by-step guide on LLM evaluation. Learn practical strategies to assess large language models, ensure business success, and minimize implementation risks.
Explore the differences in real-time and batch monitoring for LLMs. Learn which approach suits your needs for optimal performance and data insight.
Explore LLM benchmarks categories for evaluating AI. Learn about frameworks, metrics, industry use cases, and the future of language model assessment in this comprehensive guide.
Delve into the Character Error Rate metric, a pivotal tool for evaluating AI precision at the character level. Explore its applications, calculations, and impact.
Learn how to evaluate agent contributions in dynamic multi-agent workflows. Unlock insights with effective metrics and optimize collaboration efficiently.
Explore key benchmarks for evaluating multi-agent AI. Discover their strengths, weaknesses, and how to choose the best one for your needs.
Explore the role of Semantic Textual Similarity (STS) metric in AI, from concept to real-world applications and challenges.
Learn the fundamentals of AI agent architecture, including key components, challenges, and best practices for building functional, secure, and scalable AI systems. Discover how to optimize your AI with expert insights and tools.
Understanding LLM Performance Metrics informs model selection and guides optimization strategies to meet specific application needs, particularly important when organizations are adopting generative AI.
Discover how self-evaluation, chain of thought, error detection, and self-reflection in AI agents enhance performance, reduce errors, and improve reliability.
Ensuring that Large Language Models (LLMs) perform well in production is crucial for successful AI deployments. Effective LLM Model Monitoring helps prevent errors, security risks, and performance issues that could hinder AI initiatives.
Discover how Agentic RAG systems integrate retrieval and generation in AI, enhancing decision-making and precision. Explore its impact across industries.
Discover BERTScore’s transformative role in AI, offering nuanced and context-aware evaluation for NLP tasks, surpassing traditional metrics.
Explore the G-Eval metric, a pivotal tool for evaluating AI creativity and coherence, enhancing real-world model performance beyond basic accuracy.
Learn how Retrieval Augmented Fine-Tuning (RAFT) revolutionizes domain-specific RAG tasks. Boost fine-tuning accuracy and performance significantly.
Elevate factual QA with robust monitoring and guardrails. Discover how Galileo ensures truthfulness and reliability in enterprise AI systems.
Discover how Cohen's Kappa metric enhances AI evaluation by measuring inter-rater agreement, ensuring data quality, and improving model reliability.
Explore the Mean Average Precision (MAP) metric for AI model evaluation. Learn its significance in ranking tasks, practical applications, and optimization strategies.
Learn the next evolution of automated AI evaluations – Evaluation Agents.
Explore dynamic environment performance testing for AI agents. Learn methodologies ensuring adaptability in real-world scenarios to boost system reliability.
Explore single-agent vs multi-agent AI systems. Understand their benefits, challenges, and real-world applications for enterprises.
Discover how RAG architecture revolutionizes AI with real-time data access. Enhance AI interactions and decision-making with our comprehensive component analysis.
Discover how AI and modern programming languages like Rust and Golang transform legacy applications, reduce technical debt, and drive innovation in today's competitive tech landscape.
Dive into the groundbreaking Llama 3 models. Discover advanced NLP, efficiency, and multilingual capabilities for AI engineers and data scientists.
Unlock the secrets of effective AI agent evaluation with our comprehensive guide. Discover key methods, overcome challenges, and implement best practices for success.
Explore the pros and cons of combining qualitative and quantitative methods to enhance LLM evaluation, ensuring comprehensive assessment and growth.
Explore effective methods to evaluate AI agents across domains, ensuring proficiency, consistency, and ethical compliance with Galileo's insights and tools.
Discover insights on AUC-ROC metrics in model evaluation. Learn calculation techniques to enhance your machine learning models.
Understand the importance of Mean Reciprocal Rank (MRR) metric in AI systems for delivering accurate, relevant results. Enhance reliability with this comprehensive guide.
Master the skills needed to build AI agents, from advanced programming to ethical handling of data. Elevate your AI projects with technical and strategic excellence.
Learn how to implement AI in your business with strategies for cost management, workforce readiness, and system integration to drive growth and efficiency.
Discover how the F1 Score provides a comprehensive evaluation of speech-to-text models beyond basic accuracy. Learn why precision and recall matter in assessing transcription performance for real-world applications.
Explore functional correctness in AI - its significance, enterprise implementation strategies, and how innovative evaluation methods enhance reliability.
Explore human-centered AI evaluation strategies that combine human judgment with automated metrics. Learn how to ensure accuracy, cultural sensitivity, and ethical AI practices using advanced tools like Galileo and inclusive evaluation frameworks.
Explore the ROUGE Metric, a key tool in assessing AI-generated summaries against human judgment. Learn its variants and integration methods.
Uncover how the Word Error Rate metric revolutionizes AI performance in speech and language processing. Essential insights for developers and tech enthusiasts.
Dive into essential data processing strategies for RAG systems. Ensure accuracy, optimize performance and explore cutting-edge techniques for enhanced retrieval.
Explore how Prompt Perplexity measures AI reliability. Learn to ensure consistent, accurate outputs & enhance model performance with Galileo's innovative metric.
Explore advanced RAG performance optimization strategies for AI engineers. Enhance retrieval processes and resource efficiency in AI systems today!
Enhance AI efficiency with top strategies for mastering multimodal models, integrating diverse data types, and ensuring secure deployments.
AGNTCY brings together industry leaders to create open standards for multi-agentic systems. We're addressing the lack of standardization, trust, and infrastructure to build a future where AI agents can seamlessly discover, compose, deploy, and evaluate each other's capabilities at scale.
Explore the cost of training LLM models, essential elements that affect expenses, and effective strategies to manage AI investments efficiently.
Explore ethical challenges in RAG systems: bias, transparency, privacy, misinformation, and accountability. Learn strategies to ensure fair applications.
Explore how the Galileo Correctness Metric enhances AI accuracy by assessing factual reliability. Boost model accuracy & align with real-world standards.
Explore the critical performance metrics and evaluation frameworks that define success in multi-agent AI systems. Learn about accuracy, fairness, and more.
Explore how to create, optimize, and evaluate agent systems for data review.
Learn to monitor and mitigate threats in multi-agent decision-making systems to enhance security and efficiency in AI-driven industries.
Discover Galileo's tool for measuring AI adherence to instructions, ensuring model performance aligns with user needs, business objectives, and safety.
Learn how AI agentic systems enhance automation through autonomous decision-making. Learn key evaluation strategies, task completion metrics, error management, and Galileo’s approach to AI performance monitoring.
Master AI model evaluation with accuracy metrics. Learn precision, recall, F1, AUC-ROC, and more for balanced & imbalanced datasets.
Learn how the BLEU Metric improves machine translation accuracy and AI model evaluations through its precise assessment criteria. Enhance multilingual projects.
This article delves into agentic AI, its frameworks, operations, and practical applications, addressing user needs from foundational understanding to advanced insights.
Explore the importance, calculation, and application of PR curves in machine learning.
Explore the intricacies of AI agentic workflows, including definitions, applications, and implementation strategies, to empower users in optimizing autonomous systems.
This article explores key strategies for evaluating Multimodal AI, covering methods to assess performance across text, images, and audio. Learn how to improve accuracy, detect errors, and ensure reliable AI systems with effective evaluation techniques.
This article discusses the biggest challenges in building and using Multimodal Large Language Models (MLLMs), such as hallucinations, evaluating performance, data integration, and real-time monitoring. It covers best practices for improving accuracy, reducing errors, and making MLLMs more reliable. If you're working with multimodal AI, this guide will help you tackle these challenges with practical solutions.
We built this leaderboard to answer one simple question: "How do AI agents perform in real-world agentic scenarios?"
Discover how Google's Gemini models unlock the power of multimodal AI—combining text, images, audio, and video—to create smarter, more intuitive applications across industries.
Continuous Learning with Human Feedback combines the scalability of automated LLM-as-a-Judge evaluations with the precision of human insights— it's a breakthrough workflow that enables automated prompt-tuning of evaluation metrics for as much as 30% improvements in accuracy with as few as 2-5 labeled records.
Unlock the power of AI with our comprehensive guide to Retrieval-Augmented Generation. Discover advanced metrics, best practices, and expert insights to enhance your AI applications.
Learn more about essential AI security strategies for GenAI systems. We outline the best practices to safeguard your AI applications from threats and vulnerabilities.
Explore how MMLU evaluates AI across 57 subjects, from STEM to humanities. Learn about testing methodologies, performance standards, and optimization.
Discover the essential AI safety metrics to secure your applications. Learn how Galileo can help you evaluate, monitor, and protect your AI systems for reliable performance.
A step-by-step guide for evaluating smart agents
Explore how AI is reshaping developer collaboration by enhancing psychological safety, boosting inclusion, and empowering diverse teams through transparent, open-source solutions.
Learn more about fluency metrics for LLM RAG systems. We cover ROUGE, BLEU, and more to help you better optimize your AI's language generation performance.
Discover how to optimize LLM parameters for better AI performance. Our guide covers key metrics, evaluation techniques, and tips for fine-tuning your models effectively.
Everything developers need to build, ship, and scale best-in-class AI agents.
Learn how to improve AI agent performance through structured evaluations, including how to evaluate tool selection, common pitfalls, and how to optimize agentic decision-making.
Learn how to implement comprehensive AI risk management in your company. Frameworks, tools, and strategies for operational excellence.
Explore the key limitations of open source LLMs, from performance gaps to evaluation challenges. Discover critical insights for AI developers and decision-makers.
Join Conor Bronsdon as he chats with Galileo co-founders Yash Sheth (COO) and Atindriyo Sanyal (CTO) about major trends to look for this year. These include AI finding its product "tool stack" fit, generation latency decreasing, AI agents, their potential to revolutionize code generation and other industries, and the crucial role of robust evaluation tools in ensuring the responsible and effective deployment of these agents.
Unlock the power of BLANC Metric for AI document summarization. Learn how to evaluate and improve your AI's performance with this cutting-edge technique.
Effective human assistance in AI agents
"This is the time. This is the time to start building... I can't say that often enough. This is the time." - Bob van Luijt Join Bob van Luijt, CEO and co-founder of Weaviate as he sits down with our host Conor Bronson for the Season 2 premiere of Chain of Thought. Together, they explore the ever-evolving world of AI infrastructure and the evolution of Retrieval-Augmented Generation (RAG) architecture.
Unlock the key to AI agent testing with our guide. Discover metrics, best practices, and innovative techniques to evaluate your AI agents.
Discover how to evaluate AI agents in real-world scenarios through benchmarks. Our guide explores key benchmark types, performance metrics, and insights for optimizing AI agents.
Whether you’re diving into the world of autonomous agents for the first time or just need a quick refresher, this blog breaks down the different levels of AI agents, their use cases, and the workflow running under the hood.
Discover how AI assistants function as "async junior digital employees," taking on specific tasks and contributing to the organizational structure
Top research benchmarks for evaluating agent performance for planning, tool calling and persuasion.
Explore the challenges and opportunities of deploying GenAI at enterprise scale in a conversation that's a wake-up call for any business leader looking to harness the power of AI.
Learn to bridge the gap between AI capabilities and business outcomes
Learn the key concepts behind multimodal AI evaluation, why multimodality is more challenging than text-based evaluations, and what to consider in your evaluation framework.
As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential. Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thought we’re joined by Chip Huyen (Storyteller, Tép Studio), Vivienne Zhang (Senior Product Manager, Generative AI Software, Nvidia) for a discussion on AI evaluation best practices
Discover how ROUGE evaluates AI text summarization. Learn to optimize your AI models with this key metric for better performance.
Discover why explainability matters in AI and how to achieve it. Unlock transparency, build trust, and create reliable AI solutions with practical insights.
Fluency in AI: Mastering Generative Systems | Galileo
A comprehensive guide to metrics for GenAI chatbot agents
The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems. Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment?” This week on Chain of Thought, Galileo’s CEO, Vikram Chatterji joins Conor Bronsdon to discuss AI's value proposition, from the initial hype to the current search for tangible returns, offering insights into how businesses can identify the right AI use cases to maximize their investment.
Discover strategies for engineering leaders to successfully navigate AI challenges, balance business pressures, and implement effective AI adoption frameworks.
Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge? This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025
In the field of artificial intelligence, selecting the right model architecture is crucial for your project's success. For AI developers and CTOs comparing different architectures, knowing the differences between Retrieval-Augmented Generation (RAG) and traditional Large Language Models (LLMs) helps in building effective AI applications. Many organizations, from healthcare to finance, rely on real-time, accurate data for decision-making. Retrieval-Augmented Generation (RAG) offers a solution for these use cases by integrating external knowledge during inference, providing access to current data that traditional LLMs lack due to static training.
Do you want to enhance your AI models with Retrieval-Augmented Generation (RAG)? This article discusses the top tools that data scientists, AI engineers, and developers use to build efficient, accurate, and context-aware RAG systems.
Managing Large Language Models (LLMs) effectively requires good monitoring to ensure they are reliable and perform well. This guide compares how Datadog LLM Monitoring and Galileo's specialized LLM monitoring solutions can help you manage and improve your AI applications.
Are you deciding between using large language models (LLMs) and traditional NLP models for your next AI project? This article explores LLM vs. NLP Models, helping you understand the key differences and make an informed choice that suits your needs.
Choosing the right speech-to-text tool is crucial for enhancing communication, accessibility, and efficiency across various industries. However, with the rapid advancements in real-time speech-to-text technology, it can be challenging to determine which solution best suits your needs. This guide will help you understand these tools, the key features to look for, and how to select the one that aligns with your specific requirements and workflows.
Speech-to-Text for Enterprises plays a crucial role in helping organizations improve productivity and gain insights through accurate and scalable transcription systems.
Optimizing RAG Performance is essential for AI engineers to enhance efficiency and accuracy in their Retrieval-Augmented Generation systems. Slow responses and irrelevant outputs can hinder user experience and application success. This guide offers best practices and strategies to improve the speed and accuracy of your RAG system.
AI agents have quickly emerged as the next ‘hot thing’ in AI, but what constitutes an AI agent and do they live up to the hype?
Identify issues quickly and improve agent performance with powerful metrics
From ChatGPT's search engine to Google's AI-powered code generation, artificial intelligence is transforming how we build and deploy technology. In this inaugural episode of Chain of Thought, the co-founders of Galileo explore the state of AI, from open-source models to establishing trust in enterprise applications. Plus, tune in for a segment on the impact of the Presidential election on AI regulation. The episode culminates with an interview of May Habib, CEO of Writer, who shares practical insights on implementing generative AI at scale.
Join us at AWS re:Invent to see the latest in AI evaluation intelligence and learn from leading GenAI experts!
Evaluating large language models (LLMs) has become a critical task for data scientists and AI professionals. Understanding effective evaluation metrics and frameworks is key to ensuring the reliability and accuracy of these models in real-world applications.
LLMs are being deployed in critical applications across various industries. In these real-world applications, ensuring the reliability and performance of AI models is paramount, as errors or unexpected behaviors can lead to significant consequences.
Evaluating the critical thinking capabilities of Large Language Models (LLMs) is important for developers and data scientists who want to build reliable AI systems. Knowing which benchmarks assess these abilities helps engineers integrate AI into their applications. In this article, we'll explore the top benchmarks for evaluating LLMs' critical thinking skills and compare tools like Galileo, Patronus, and Langsmith.
AI models now influence critical decisions and daily life, so ensuring their accuracy and reliability is essential. Explore AI model validation to master techniques that keep your models effective and trustworthy, using tools like Galileo for the best results.
In the field of artificial intelligence, understanding the differences between LLM Monitoring vs. Observability is important for data scientists, AI practitioners, and enterprise teams who want to improve the performance, reliability, and safety of their generative AI systems.
As full-stack engineers exploring AI, understanding how to evaluate Large Language Models (LLMs) is essential for developing accurate and reliable AI applications. In this article, we'll discuss building an effective LLM evaluation framework from scratch, exploring methods to assess and enhance your models by leveraging insights on LLM applications, comparing different evaluation tools, and showing how Galileo provides a complete solution.
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
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.
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.
Understand the most common issues with AI agents in production.
Learn to create and filter synthetic data with ChainPoll for building evaluation and training dataset
Select the best framework for building intelligent AI Agents
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.
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.
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.
