evaluation

In: Artificial Intelligence

In the media and entertainment industry, understanding and predicting the effectiveness of marketing campaigns is crucial for success. Marketing campaigns are the driving force behind successful businesses, playing a pivotal role in attracting new customers, retaining existing ones, and ultimately boosting revenue. However, launching a campaign isn’t enough; to maximizeContinue Reading

In: Artificial Intelligence

Large language models (LLMs) have become integral to numerous applications across industries, ranging from enhanced customer interactions to automated business processes. Deploying these models in real-world scenarios presents significant challenges, particularly in ensuring accuracy, fairness, relevance, and mitigating hallucinations. Thorough evaluation of the performance and outputs of these models isContinue Reading

In: Artificial Intelligence

Developing generative AI agents that can tackle real-world tasks is complex, and building production-grade agentic applications requires integrating agents with additional tools such as user interfaces, evaluation frameworks, and continuous improvement mechanisms. Developers often find themselves grappling with unpredictable behaviors, intricate workflows, and a web of complex interactions. The experimentationContinue Reading

In: Artificial Intelligence

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle. As these AI technologies become more sophisticated and widely adopted, maintaining consistent quality and performance becomes increasinglyContinue Reading

In: Artificial Intelligence

Evaluating large language models (LLMs) is crucial as LLM-based systems become increasingly powerful and relevant in our society. Rigorous testing allows us to understand an LLM’s capabilities, limitations, and potential biases, and provide actionable feedback to identify and mitigate risk. Furthermore, evaluation processes are important not only for LLMs, butContinue Reading

In: Artificial Intelligence

This post was co-written with Varun Kumar from Tealium Retrieval Augmented Generation (RAG) pipelines are popular for generating domain-specific outputs based on external data that’s fed in as part of the context. However, there are challenges with evaluating and improving such systems. Two open-source libraries, Ragas (a library for RAGContinue Reading

In: Artificial Intelligence

Generative artificial intelligence (AI), particularly Retrieval Augmented Generation (RAG) solutions, are rapidly demonstrating their vast potential to revolutionize enterprise operations. RAG models combine the strengths of information retrieval systems with advanced natural language generation, enabling more contextually accurate and informative outputs. From automating customer interactions to optimizing backend operation processes,Continue Reading