Best of LLM — November 2024

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   Video

   3Blue1Brown·1y

   Large Language Models explained briefly

   The post explains large language models (LLMs), how they function, and the complexities behind their training. LLMs predict the next word in a sequence based on probabilities, using vast amounts of text data for training. The introduction of transformers in 2017 allowed for parallel processing of text, enhancing computation efficiency. Pre-training is supplemented by reinforcement learning with human feedback to refine model predictions. The sheer scale of data and computation involved is formidable, taking advantage of specialized hardware like GPUs.

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   Video

   YouTube·1y

   This is how I scrape 99% websites via LLM

   Explore how advancements in AI, particularly large language models (LLMs), are revolutionizing web scraping in 2024. Learn the best practices for scripting internet data at a large scale, building autonomous web scrapers, and handling complex web interactions. The post demonstrates various kinds of web scraping tasks, including scraping public websites, handling complex web manipulations, and more sophisticated tasks that require reasoning. It also includes details about services like OpenAI, AgentQL, and SpiderCloud that facilitate optimized web content extraction.

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   Article

   Machine Learning Mastery·1y

   5 Free Courses for Mastering LLMs

   Large Language Models (LLMs) have become a significant breakthrough in AI, excelling in understanding and generating human-like text. This post highlights five free courses to help learners master LLMs. Courses include an introduction by Google, an AI for Educators course by Microsoft, a technical deep dive from Cohere’s LLM University, prompt engineering courses by Anthropic, and a detailed LLM agents course by UC Berkeley and Google DeepMind. These courses cater to a range of learners from beginners to those looking to develop expertise in LLM applications and prompt engineering.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   A Crash Course on Building RAG Systems – Part 4

   Part 4 of the crash course on building RAG systems focuses on implementing RAG on multimodal data, specifically complex documents with tables, texts, and images. This series covers foundational components, evaluation methods, optimization techniques, and handling large data sets, making it highly beginner-friendly. Understanding how to build reliable RAG systems can reduce costs and enhance scalability for enterprises, bypassing the need for fine-tuning large language models (LLMs).

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   Article

   Uber Engineering·1y

   Introducing the Prompt Engineering Toolkit

   A well-crafted prompt is vital for obtaining accurate outputs from Large Language Models (LLMs). To streamline this process, Uber developed a Prompt Engineering Toolkit that centralizes prompt template creation, management, and evaluation. The toolkit supports context enrichment, batch generation, version control, and safety measures to ensure responsible AI use. It includes a GenAI Playground for prompt exploration and advanced guidance techniques to enhance prompt quality. The toolkit is designed to facilitate LLM usage across development and production stages, offering a robust framework for effective prompt engineering.

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   Article

   Towards AI·1y

   Apple: LLMs CANNOT Reason

   Apple's recent research suggests that large language models (LLMs) can't truly reason but instead rely on pattern recognition from their training data. Tests showed that slight modifications in problem statements led to decreased performance, especially in smaller models. Larger models resisted these changes better but still showed some decline, indicating they mimic reasoning but don't fully understand the logic. An intriguing experiment revealed that LLMs can be misled by irrelevant information, exposing their limitations in logical thinking.

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   Article

   SwirlAI·1y

   What is AI Engineering?

   AI Engineering is a rapidly evolving role focused on developing and deploying AI systems that utilize Large Language Models (LLMs) to solve business problems. AI Engineers differ from Software Engineers and Machine Learning Engineers in that they deal extensively with non-deterministic systems and require skills in prompt engineering, infrastructure, and data integration. The field is witnessing the rise of Agentic systems, which are advanced AI systems capable of performing complex tasks with a degree of autonomy. AI Engineering is poised to become one of the most in-demand roles in the tech industry with high salaries and growing opportunities.

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   Article

   Collections·1y

   Why There’s No Better Time to Learn LLM Development

   The rapid evolution of Large Language Models (LLMs) offers significant efficiency gains, making it an ideal time to learn LLM development. The comprehensive guide *Building LLMs for Production* helps bridge the skill gap for aspiring developers. Key techniques covered include prompting, fine-tuning, and data preparation. The updated edition offers new chapters on data intricacies, and indexes and retrievers, ensuring developers have the latest insights and practices. The guide is available at a discounted rate on the Towards AI Academy platform.

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   Article

   Weaviate·1y

   What is Agentic RAG

   Agentic RAG is an advanced AI framework enhancing the traditional Retrieval-Augmented Generation (RAG) pipelines by incorporating AI agents. These agents possess memory, planning, and tool capabilities to perform various actions beyond simple information retrieval. The architecture can range from single-agent systems acting as routers to complex multi-agent setups coordinating multiple specialists. This approach addresses the limitations of vanilla RAG by providing tools, multi-step retrieval, and validation, thereby improving response accuracy and robustness, while introducing potential latency and reliability issues inherent to LLMs.

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    Article

    Towards AI·1y

    #50 Why Do Neural Networks Hallucinate?

    Towards AI offers a comprehensive course aimed at transforming students from beginners to advanced LLM developers. The course includes over 85 lessons and covers everything from choosing suitable LLM applications to advanced techniques and deployment. It uses popular tools like OpenAI, Llama 3, and Hugging Face. The course also emphasizes non-technical skills and offers instructor support on Discord. Additionally, this post features community projects, an AI poll, and articles on predictive modeling and AI hallucinations.

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    Article

    Daily Dose of Data Science | Avi Chawla | Substack·1y

    Building a Multi-agent Financial Analyst

    The post demonstrates building a multi-agent financial analyst using Microsoft's Autogen and Llama3-70B. It outlines the tech stack, including the roles of code executor and code writer agents. The guide provides steps to set up the agents, execute code, and display stock analysis results. Additional resources and a GitHub repository for further exploration are also mentioned.

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    Article

    Daily Dose of Data Science | Avi Chawla | Substack·1y

    A Crash Course on Building RAG Systems – Part 2

    Gain expertise in implementing RAG systems with this beginner-friendly guide. Part 2 builds on the foundations of Part 1, focusing on practical implementation. Learn how RAG systems address challenges in NLP and help bypass the costs of fine-tuning LLMs, offering enterprises significant cost savings. This crash course covers essential techniques and practical guidance for building reliable RAG applications.

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    Article

    NVIDIA Developer·1y

    Mastering LLM Techniques: Data Preprocessing

    Large language models (LLMs) significantly enhance efficiency by automating tasks, but their performance heavily depends on high-quality data. Effective data preprocessing—such as text cleaning, deduplication, and quality filtering—is crucial to ensure optimal model accuracy. Techniques like leveraging synthetic data generation and tools like NVIDIA NeMo Curator can help overcome common challenges such as data scarcity, reducing toxics, and managing vast datasets efficiently. NeMo Curator's use of GPU-accelerated libraries enhances the speed and efficiency of data processing workflows.

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    Article

    Medium·1y

    Treating AI Agents as personas

    AI agents are transforming from tools into active participants in digital environments, prompting a new era of Agent-Computer Interaction where UX design must consider both human and AI user needs. AI agents can now navigate graphical user interfaces, performing complex tasks autonomously. Designers should develop personas for AI agents, shape their behavior through effective prompting, and ensure transparency and user control to enhance human-AI interactions.

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    Article

    Hacker News·1y

    gregpr07/browser-use

    Browser-Use allows Language Models (LLMs) to interact with websites naturally, offering features like universal LLM support, smart element detection, multi-tab management, and vision model support. Users can customize browser interactions and persist browser states across multiple agents. It supports all LangChain chat models and provides examples and quick start guides to help users get started.

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    Article

    GoPenAI·1y

    Refining RAG Accuracy with TrueLens: An Evaluation Guide

    In today's AI landscape, Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by leveraging user-specific data for context-driven responses. To ensure quality, rigorous evaluation frameworks like TruLens are essential. This guide explores the use of TruLens's feedback functions to assess context relevance, groundedness, and answer relevance, helping to improve RAG pipelines by minimizing risks such as hallucinations and biases. The step-by-step instructions illustrate how to set up and evaluate a RAG pipeline, ensuring consistency and high performance in AI-driven responses.

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    Article

    Daily Dose of Data Science | Avi Chawla | Substack·1y

    Traditional RAG vs. HyDE

    Traditional RAG systems often retrieve irrelevant contexts due to questions not being semantically similar to their answers. HyDE mitigates this by generating a hypothetical answer to the query and embedding it using a contriever model to fetch more relevant contexts. While this improves retrieval performance, it comes with increased latency and more LLM usage.

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    Article

    Towards AI·1y

    Why Become an LLM Developer? Launching Towards AI’s New One-Stop Conversion Course

    The post discusses the importance and emerging role of LLM (Large Language Model) Developers in the modern AI-driven economy. It introduces a new course by Towards AI that aims to equip learners with essential technical and non-technical skills for developing customized LLM pipelines. The course covers core techniques such as Prompt Engineering, Retrieval-Augmented Generation (RAG), Fine-Tuning, and more, and is designed for Software Developers, Machine Learning Engineers, Data Scientists, and aspiring founders. The course emphasizes practical application through various tools and frameworks, aiming to produce reliable and efficient LLM-based products.

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    Article

    Towards Data Science·1y

    Building Knowledge Graphs with LLM Graph Transformer

    This post explores building knowledge graphs using the LLM Graph Transformer from LangChain. It covers techniques for extracting structured data from unstructured text to create knowledge graphs, highlighting the advantages and challenges of both tool-based and prompt-based modes. The guide includes steps for setting up a Neo4j environment, defining graph schemas, and ensuring consistency in extraction. Additionally, it addresses how to import graph documents into databases like Neo4j for further analysis and application.

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    Video

    Community Picks·1y

    Why is everyone LYING?

    The post discusses skepticism regarding the claims that AI-powered tools, particularly large language models (LLMs) like Claude, can enhance coding speed and ability to develop apps significantly. The author shares their experience and criticisms, arguing that LLMs still struggle with complex tasks and often require significant human intervention to produce functional and error-free code. This challenges the idea that non-technical founders can easily create tech companies with the help of LLMs.

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    Article

    Towards AI·1y

    Evaluating and Monitoring LLM Agents: Tools, Metrics, and Best Practices

    The post discusses the tools and metrics for evaluating and monitoring LLM agents using the agentic approach. It highlights the role of Retrieval-Augmented Generation (RAG) pipelines, and tools like Arize Phoenix, ragas, and TrueLens in the evaluation process. The agentic approach allows LLMs to collaborate seamlessly on tasks with minimal human intervention, emphasizing the importance of orchestration platforms and tools for effective task management.

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    Article

    NVIDIA Developer·1y

    Build Your First Human-in-the-Loop AI Agent with NVIDIA NIM

    Learn how to build a human-in-the-loop AI agent using NVIDIA NIM microservices to streamline and enhance content creation workflows. This tutorial demonstrates the use of AI agents for generating promotional content and visuals, while maintaining human oversight to ensure quality and creativity. Incorporate advanced LLMs into your processes and accelerate your tasks with scalable and flexible AI-driven solutions.

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    Article

    Machine Learning News·1y

    Top 5 Effective Design Patterns for LLM Agents in Real-world Applications

    Effective AI agent design is crucial for real-world applications. Anthropic identifies five key design patterns for Large Language Models (LLMs) like Claude's: Delegation improves efficiency through parallel processing; Parallelization balances cost and speed; Specialization allows for expert-driven responses; Debate enhances decision-making through agent discussions; and Tool Suite Experts ensure effective use of complex toolsets. These strategies optimize AI systems for performance, responsiveness, and accuracy in various applications.

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    Article

    Data Science Central·1y

    There is no such thing as a Trained LLM

    Traditional LLMs are often trained on tasks that don't align with their actual use cases, leading to inefficiencies and unnecessary complexity. The notion that training is essential for LLMs is challenged, suggesting that unsupervised learning and specialized architectures might provide better results. Various evaluation metrics and overlooked criteria like exhaustivity, inference, and ease of use are discussed. The article introduces xLLM, a next-gen architecture that emphasizes efficiency and user-friendly features, potentially eliminating the need for extensive training.

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    Article

    Medium·1y

    Why LLM watermarking will never work

    The post argues that watermarking, or the process of embedding detectable patterns in AI-generated text, is ineffective in distinguishing between AI-generated and human-generated content. It discusses three critical conditions that render watermarking ineffective: the existence of capable LLMs that don't implement watermarking, the allowance of user control over token selection, and the availability of open-source models. The author questions the true goals behind watermarking and suggests that focusing on detecting harmful content directly would be more effective in reducing AI-generated harms.

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