Machine Learning

🤖🧠 DeepEval: The Ultimate LLM Evaluation Framework for AI Developers

🗓️ 07 Oct 2025
📚 AI News & Trends

In today’s AI-driven world, large language models (LLMs) have become central to modern applications from chatbots to intelligent AI agents. However, ensuring the accuracy, reliability and safety of these models is a significant challenge. Even small errors, biases or hallucinations can result in misleading information, frustrated users or business setbacks. This is where DeepEval, an ...

#DeepEval #LLM #AIDevelopment #LanguageModels #ModelEvaluation #ArtificialIntelligence

296 views08:17

🤖🧠 Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs

🗓️ 08 Oct 2025
📚 AI News & Trends

In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate today’s AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...

#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides

303 views10:17

🤖🧠 Mastering Large Language Models: Top #1 Complete Guide to Maxime Labonne’s LLM Course

🗓️ 22 Oct 2025
📚 AI News & Trends

In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...

#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels

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🤖🧠 LangChain: The Ultimate Framework for Building Reliable AI Agents and LLM Applications

🗓️ 24 Oct 2025
📚 AI News & Trends

As artificial intelligence continues to transform industries, developers are racing to build smarter, more adaptive applications powered by Large Language Models (LLMs). Yet, one major challenge remains how to make these models interact intelligently with real-world data and external systems in a scalable, reliable way. Enter LangChain, an open-source framework designed to make LLM-powered application ...

#LangChain #AI #LLM #ArtificialIntelligence #OpenSource #AIAgents

961 views15:56

🤖🧠 LangExtract by Google: Transforming Unstructured Text into Structured Data with LLM Precision

🗓️ 27 Oct 2025
📚 AI News & Trends

In the world of data-driven decision-making, one of the biggest challenges lies in extracting meaningful insights from unstructured text — documents, reports, emails or articles that lack consistent structure. Manually organizing this information is both time-consuming and prone to errors. Enter LangExtract, an advanced Python library by Google that leverages Large Language Models (LLMs) like ...

#LangExtract #LLM #StructuredData #UnstructuredText #PythonLibrary #GoogleAI

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525 views16:29

📌 How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-05 | ⏱️ Read time: 9 min read

Enhance your RAG pipeline's performance by effectively evaluating its retrieval quality. This guide, the second in a series, explores the use of key binary, order-aware metrics. It provides a detailed look at Mean Reciprocal Rank (MRR) and Average Precision (AP), essential tools for ensuring your system retrieves the most relevant information first and improves overall accuracy.

#RAG #LLM #AIEvaluation #MachineLearning

928 views06:29

📌 Multi-Agent SQL Assistant, Part 2: Building a RAG Manager

🗂 Category: AI APPLICATIONS

🕒 Date: 2025-11-06 | ⏱️ Read time: 21 min read

Explore building a multi-agent SQL assistant in this hands-on guide to creating a RAG Manager. Part 2 of this series provides a practical comparison of multiple Retrieval-Augmented Generation strategies, weighing traditional keyword search against modern vector-based approaches using FAISS and Chroma. Learn how to select and implement the most effective retrieval method to enhance your AI assistant's performance and accuracy when interacting with databases.

#RAG #SQL #AI #VectorSearch #LLM

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957 views04:30

🤖🧠 Kimi Linear: The Future of Efficient Attention in Large Language Models

🗓️ 08 Nov 2025
📚 AI News & Trends

The rapid evolution of large language models (LLMs) has unlocked new capabilities in natural language understanding, reasoning, coding and multimodal tasks. However, as models grow more advanced, one major challenge persists: computational efficiency. Traditional full-attention architectures struggle to scale efficiently, especially when handling long context windows and real-time inference workloads. The increasing demand for agent-like ...

#KimiLinear #EfficientAttention #LargeLanguageModels #LLM #ComputationalEfficiency #AIInnovation

656 views20:33

📌 Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-11 | ⏱️ Read time: 15 min read

Go beyond the hype with this practitioner's guide to GraphRAG. This article offers a critical perspective on the advanced RAG technique, exploring essential design best practices, common challenges, and key learnings from real-world implementation. It provides a framework to help you decide if GraphRAG is the right solution for your specific needs, moving past the buzz to focus on practical application.

#GraphRAG #RAG #AI #KnowledgeGraphs #LLM

665 views22:30

📌 The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)

🗂 Category: DATA SCIENCE

🕒 Date: 2025-11-11 | ⏱️ Read time: 10 min read

This article charts the evolution of the data scientist's role through three distinct eras: traditional machine learning, deep learning, and the current age of large language models (LLMs). Using a single, practical use case, it illustrates how the approach to problem-solving has shifted with each technological generation. The piece serves as a guide for practitioners, clarifying when to leverage classic algorithms, complex neural networks, or the latest foundation models, helping them select the most appropriate tool for the task at hand.

#DataScience #MachineLearning #DeepLearning #LLM

702 views02:30

📌 How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-12 | ⏱️ Read time: 8 min read

This final part of the series on RAG pipeline evaluation explores advanced metrics for assessing retrieval quality. Learn how to use Discounted Cumulative Gain (DCG@k) and Normalized Discounted Cumulative Gain (NDCG@k) to measure the relevance and ranking of retrieved documents, moving beyond simpler metrics for a more nuanced understanding of your system's performance.

#RAG #EvaluationMetrics #LLM #InformationRetrieval #MLOps

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1.02K views09:57

📌 Why LLMs Aren’t a One-Size-Fits-All Solution for Enterprises

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-18 | ⏱️ Read time: 10 min read

While Large Language Models (LLMs) excel at extracting value from unstructured enterprise data, they are not a one-size-fits-all solution. Adopting this technology requires a nuanced strategy that considers specific business needs, data privacy, and model customization. For enterprises, understanding the limitations of LLMs is as crucial as recognizing their potential, ensuring a tailored approach is taken to achieve real-world ROI and avoid common implementation pitfalls.

#LLM #EnterpriseAI #AIStrategy #GenAI

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981 views04:47

📌 How Relevance Models Foreshadowed Transformers for NLP

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-11-20 | ⏱️ Read time: 19 min read

The revolutionary attention mechanism at the heart of modern transformers and LLMs has a surprising history. This article traces its lineage back to "relevance models" from the field of information retrieval. It explores how these earlier models, designed to weigh the importance of terms, laid the conceptual groundwork for the attention mechanism that powers today's most advanced NLP. This historical perspective highlights how today's breakthroughs are built upon foundational concepts, reminding us that innovation often stands on the shoulders of giants.

#NLP #Transformers #LLM #AttentionMechanism #AIHistory

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1.14K views20:24

📌 How to Use Gemini 3 Pro Efficiently

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-20 | ⏱️ Read time: 8 min read

Unlock the full potential of Gemini 3 Pro. This guide explores efficient usage techniques, delving into the model's pros and cons based on rigorous testing in coding and other demanding applications. Learn best practices to optimize your workflows and harness the full power of this advanced AI for superior results.

#Gemini3Pro #AI #GoogleAI #PromptEngineering #LLM

1.1K views06:35

📌 Your Next ‘Large’ Language Model Might Not Be Large After All

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-11-23 | ⏱️ Read time: 11 min read

A paradigm shift may be underway in AI, as a compact 27M-parameter model has outperformed industry giants like DeepSeek R1, o3-mini, and Claude 3.7 on complex reasoning tasks. This breakthrough challenges the "bigger is better" philosophy for language models, signaling a significant trend towards smaller, more efficient, and highly capable models. This development suggests future advancements may focus on architectural innovation and training efficiency over sheer parameter count.

#AI #LLM #SLM #ModelEfficiency

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1.78K views00:39

📌 LLM-as-a-Judge: What It Is, Why It Works, and How to Use It to Evaluate AI Models

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-24 | ⏱️ Read time: 9 min read

Explore the 'LLM-as-a-Judge' framework, a novel approach for evaluating AI systems. This guide explains how to use large language models as automated judges to assess model performance and ensure AI quality control. It provides a step-by-step breakdown of the methodology, explores the reasons behind its effectiveness, and shows you how to implement this powerful evaluation technique.

#AIEvaluation #LLM #MLOps #LLMasJudge

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1.4K views10:41

📌 Ten Lessons of Building LLM Applications for Engineers

🗂 Category: LLM APPLICATIONS

🕒 Date: 2025-11-25 | ⏱️ Read time: 22 min read

Drawing from two years of hands-on experience, this article outlines ten essential lessons for engineers building applications with Large Language Models. Gain practical insights and field-tested advice on structuring projects, optimizing workflows, and implementing effective evaluation strategies to successfully navigate the complexities of LLM development. This guide is for engineers looking to move from theory to production-ready applications.

#LLM #AIdevelopment #SoftwareEngineering #MLOps

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1.33K views04:42

📌 Why We’ve Been Optimizing the Wrong Thing in LLMs for Years

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-28 | ⏱️ Read time: 14 min read

LLM development may have been focused on the wrong optimization targets for years. A new analysis reveals that a simple shift in the training process is the key to unlocking significant improvements. This approach reportedly leads to models with enhanced foresight, faster inference speeds, and substantially better reasoning abilities, challenging conventional development practices.

#LLM #AITraining #ModelOptimization #AI #Inference

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1.31K views20:46

📌 How to Scale Your LLM usage

🗂 Category: AGENTIC AI

🕒 Date: 2025-11-29 | ⏱️ Read time: 7 min read

Effectively scaling your Large Language Model (LLM) usage is crucial for unlocking major productivity improvements. This guide outlines key strategies for expanding LLM integration from proof-of-concept to full-scale deployment, enabling your teams to harness the full power of AI for enhanced operational efficiency and innovation. Learn the best practices for managing costs, ensuring reliability, and maximizing the impact of LLMs across your organization.

#LLM #AIScaling #Productivity #ArtificialIntelligence

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1.52K views14:47