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In this blog, we’ll explore how DoorDash leverages Retrieval-Augmented Generation (RAG) to address Dashers’ queries using insights from the company’s internal blogs. We’ll conduct practical experiments based on the techniques discussed in DoorDash’s Tech blog, utilizing a curated DoorDash dataset. We will use Athina AI which
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After completing the development and validation phase, model deployment is the next important step in the LLMOps workflow. Successful deployment requires careful consideration of efficiency, scalability, and the specific needs of the deployment environment. Quantization: Optimization Technique for Efficient Deployment of LLMs Quantization is a technique used to reduce LLMs&
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Managing large language models (LLMs) through LLMOps workflows requires a structured approach to ensure effective development, deployment, and maintenance. These workflows are divided into several key phases: data collection, model development, deployment, and governance, each crucial to the performance and sustainability of LLM systems. Specific methodologies, tools, and best practices
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The world is experiencing a transformative wave driven by large language models (LLMs). These advanced AI models, capable of understanding and generating human-quality text, are changing interactions with technology and information. LLMs power various applications, from chatbots and virtual assistants to content creation tools, advanced search engines, and even personalized
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The new Claude Sonnet 3.5 model demonstrates significant improvements, particularly in mathematical reasoning, coding, visual understanding, and agentic tool use. These enhancements make it better at handling complex problems and more versatile across various tasks. Users can expect improved accuracy with this version. In this tutorial, we will guide
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[Why Human Evaluation and Annotation is Important to Build Reliable LLM Systems] Human evaluation is a critical part of building reliable, controlled, and responsible AI. This ensures that your AI models are aligned with human criteria. Similarly, human annotation is important to: * Construct and audit automatic benchmarks * Create training data
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Retrieval-Augmented Generation (RAG) is becoming popular for building applications with large language models (LLMs). RAG improves generative models by using external information to make their responses more accurate and context-aware. This guide explains how basic RAG works and how to evaluate the RAG pipeline with specific metrics, using simple steps
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Introduction OpenAI's mission to create artificial general intelligence (AGI) that is smarter than humans is still futuristic. However, tools like BabyAGI and AutoGPT - AI agents powered by LLMs - have already raised concerns. These AI agents use LLM's reasoning engine to plan their thinking journey
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Introduction By now, you’ve surely heard about Chain-of-Thought prompting. The technique was popularized by the 2022 paper Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, which found that asking an LLM to “think step-by-step” elicits much better reasoning abilities. This technique is particularly effective at problems that require complex
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Introduction In an era where data doubles every two years, sifting through mountains of information to find exactly what you need can feel like searching for a needle in an ever-expanding haystack. Yet, this is the challenge that modern artificial intelligence (AI) systems, like Retrieval-Augmented Generation (RAG), are designed to