mailitics

Category: llm

  • From Tokens to Theorems: Building a Neuro-Symbolic AI Mathematician

    From Tokens to Theorems: Building a Neuro-Symbolic AI Mathematician The next Gauss may not be born — they may be spun up in the cloud The post From Tokens to Theorems: Building a Neuro-Symbolic AI Mathematician appeared first on Towards Data Science. Sean Moran Go to original source

  • The End-to-End Data Scientist’s Prompt Playbook

    The End-to-End Data Scientist’s Prompt Playbook Part 3: Prompts for docs, DevOps, and stakeholder communication The post The End-to-End Data Scientist’s Prompt Playbook appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs

    Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs How timeouts, truncation, and result sanitization keep Cypher outputs LLM-ready The post Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • How to Context Engineer to Optimize Question Answering Pipelines

    How to Context Engineer to Optimize Question Answering Pipelines Learn how to apply context engineering to enhance your question answering systems. The post How to Context Engineer to Optimize Question Answering Pipelines appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Should We Use LLMs As If They Were Swiss Knives?

    Should We Use LLMs As If They Were Swiss Knives? A logic game performance comparison between popular LLMs and a custom-made algorithm The post Should We Use LLMs As If They Were Swiss Knives? appeared first on Towards Data Science. Nicolas Garcia Aramouni Go to original source

  • How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques

    How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques Optimize your AI search with RAG, contextual retrieval and evaluations The post How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • What is Universality in LLMs? How to Find Universal Neurons

    What is Universality in LLMs? How to Find Universal Neurons How independently trained transformers form same the neurons The post What is Universality in LLMs? How to Find Universal Neurons appeared first on Towards Data Science. Shuyang Go to original source

  • How to Develop a Bilingual Voice Assistant

    How to Develop a Bilingual Voice Assistant Exploring ways to make voice assistants more personal The post How to Develop a Bilingual Voice Assistant appeared first on Towards Data Science. Deepak Krishnamurthy Go to original source

  • A Brief History of GPT Through Papers

    A Brief History of GPT Through Papers Language models are becoming really good. But where did they come from? The post A Brief History of GPT Through Papers appeared first on Towards Data Science. Rohit Pandey Go to original source

  • How to Develop Powerful Internal LLM Benchmarks

    How to Develop Powerful Internal LLM Benchmarks Learn how to compare LLMs using your own interal benchmark The post How to Develop Powerful Internal LLM Benchmarks appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Using Google’s LangExtract and Gemma for Structured Data Extraction

    Using Google’s LangExtract and Gemma for Structured Data Extraction Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs The post Using Google’s LangExtract and Gemma for Structured Data Extraction appeared first on Towards Data Science. Kenneth Leung Go to original source

  • How to Perform Comprehensive Large Scale LLM Validation

    How to Perform Comprehensive Large Scale LLM Validation Learn how to validate large scale LLM applications The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How We Reduced LLM Costs by 90% with 5 Lines of Code

    How We Reduced LLM Costs by 90% with 5 Lines of Code When clean code hides inefficiencies: what we learned from fixing a few lines of code and saving 90% in LLM cost. The post How We Reduced LLM Costs by 90% with 5 Lines of Code appeared first on Towards Data Science. Uri Peled Go to…

  • “Where’s Marta?”: How We Removed Uncertainty From AI Reasoning

    “Where’s Marta?”: How We Removed Uncertainty From AI Reasoning A primer on overcoming LLM limitations with formal verification. The post “Where’s Marta?”: How We Removed Uncertainty From AI Reasoning appeared first on Towards Data Science. Jacopo Tagliabue Go to original source

  • How to Create Powerful LLM Applications with Context Engineering

    How to Create Powerful LLM Applications with Context Engineering Improve your LLM by optimizing its context The post How to Create Powerful LLM Applications with Context Engineering appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • How to Use LLMs for Powerful Automatic Evaluations

    How to Use LLMs for Powerful Automatic Evaluations A beginner-friendly introduction to LLM-as-a-Judge The post How to Use LLMs for Powerful Automatic Evaluations appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • Coconut: A Framework for Latent Reasoning in LLMs

    Coconut: A Framework for Latent Reasoning in LLMs Explaining Coconut (Training Large Language Models to Reason in a Continuous Latent Space) in simple terms The post Coconut: A Framework for Latent Reasoning in LLMs appeared first on Towards Data Science. Youssef Farag Go to original source

  • Fine-Tune Your Topic Modeling Workflow with BERTopic

    Fine-Tune Your Topic Modeling Workflow with BERTopic Learn how to fine-tune BERTopic settings for more focused, reproducible, and interpretable results The post Fine-Tune Your Topic Modeling Workflow with BERTopic appeared first on Towards Data Science. Tiffany Chen Go to original source

  • Generating Structured Outputs from LLMs

    Generating Structured Outputs from LLMs An overview of popular techniques to confine LLMs’ output to a predefined schema The post Generating Structured Outputs from LLMs appeared first on Towards Data Science. Ibrahim Habib Go to original source

  • Demystifying Cosine Similarity

    Demystifying Cosine Similarity Mathematical intuition and practical considerations for NLP scenarios The post Demystifying Cosine Similarity appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Finding Golden Examples: A Smarter Approach to In-Context Learning

    Finding Golden Examples: A Smarter Approach to In-Context Learning From random example selection to systematic AuPair generation  — how to make your LLM prompts actually work The post Finding Golden Examples: A Smarter Approach to In-Context Learning appeared first on Towards Data Science. Sudheer Singh Go to original source

  • How to Evaluate Graph Retrieval in MCP Agentic Systems

    How to Evaluate Graph Retrieval in MCP Agentic Systems A framework for measuring retrieval quality in Model Context Protocol agents. The post How to Evaluate Graph Retrieval in MCP Agentic Systems appeared first on Towards Data Science. Tomaz Bratanic Go to original source

  • Talk to my Agent 

    Talk to my Agent  The exciting new world of designing conversation driven APIs for LLMs. The post Talk to my Agent  appeared first on Towards Data Science. Roni Dover Go to original source

  • How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned

    How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned A hands-on journey exploring fine-tuning techniques that unlock the power of small vision models. The post How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned appeared first on Towards Data Science. Julio Sanchez Go…

  • How To Significantly Enhance LLMs by Leveraging Context Engineering

    How To Significantly Enhance LLMs by Leveraging Context Engineering The benefits and practical aspects of context engineering for LLMs The post How To Significantly Enhance LLMs by Leveraging Context Engineering appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • The Age of Self-Evolving AI Is Here

    The Age of Self-Evolving AI Is Here How Meta’s latest breakthrough lets models learn, adapt, and improve — all on their own The post The Age of Self-Evolving AI Is Here appeared first on Towards Data Science. Moulik Gupta Go to original source

  • Your 1M+ Context Window LLM Is Less Powerful Than You Think

    Your 1M+ Context Window LLM Is Less Powerful Than You Think Why working memory is a more important bottleneck than raw context window size The post Your 1M+ Context Window LLM Is Less Powerful Than You Think appeared first on Towards Data Science. Tobias Schnabel Go to original source

  • 3 Steps to Context Engineering a Crystal-Clear Project

    3 Steps to Context Engineering a Crystal-Clear Project Learn three easy steps for gaining an intelligent picture for any project by using the skill of context engineering. The post 3 Steps to Context Engineering a Crystal-Clear Project appeared first on Towards Data Science. Kory Becker Go to original source

  • Do You Really Need a Foundation Model?

    Do You Really Need a Foundation Model? LLM or custom model: how should you choose the right solution? The post Do You Really Need a Foundation Model? appeared first on Towards Data Science. Vincent Vandenbussche Go to original source

  • How to Ensure Reliability in LLM Applications

    How to Ensure Reliability in LLM Applications Learn how to make your LLM applications more robust The post How to Ensure Reliability in LLM Applications appeared first on Towards Data Science. Eivind Kjosbakken Go to original source

  • From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment

    From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment Using optimal transport to weight what matters most In LLM-generated responses The post From Equal Weights to Smart Weights: OTPO’s Approach to Better LLM Alignment appeared first on Towards Data Science. Sudheer Singh Go to original source

  • Topic Model Labelling with LLMs

    Topic Model Labelling with LLMs Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The post Topic Model Labelling with LLMs appeared first on Towards Data Science. Petr Koráb Go to original source

  • Are You Being Unfair to LLMs?

    Are You Being Unfair to LLMs? They may deserve better. The post Are You Being Unfair to LLMs? appeared first on Towards Data Science. Julian Mendel Go to original source

  • Building a Сustom MCP Chatbot

    Building a Сustom MCP Chatbot Understanding all the details of the model context protocol The post Building a Сustom MCP Chatbot appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Your Personal Analytics Toolbox

    Your Personal Analytics Toolbox Leveraging MCP for automating your daily routine The post Your Personal Analytics Toolbox appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Fairness Pruning: Precision Surgery to Reduce Bias in LLMs

    Fairness Pruning: Precision Surgery to Reduce Bias in LLMs From unjustified shootings to neutral stories: how to fix toxic narratives with selective pruning The post Fairness Pruning: Precision Surgery to Reduce Bias in LLMs appeared first on Towards Data Science. Pere Martra Go to original source

  • A Developer’s Guide to Building Scalable AI: Workflows vs Agents

    A Developer’s Guide to Building Scalable AI: Workflows vs Agents A practical guide to choosing between AI agents and workflows for production systems, covering the hidden costs, architectural trade-offs, and decision framework that can save you thousands in deployment mistakes. Includes real-world examples and a scoring system to determine which approach fits your specific use…

  • How to Train a Chatbot Using RAG and Custom Data

    How to Train a Chatbot Using RAG and Custom Data Retrieval-Augmented Generation made easy with Llama The post How to Train a Chatbot Using RAG and Custom Data appeared first on Towards Data Science. Haden Pelletier Go to original source

  • Data Has No Moat!

    Data Has No Moat! Only if you ignore data quality The post Data Has No Moat! appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • Agentic AI: Implementing Long-Term Memory

    Agentic AI: Implementing Long-Term Memory The problem and current solutions The post Agentic AI: Implementing Long-Term Memory appeared first on Towards Data Science. Ida Silfverskiöld Go to original source

  • Why Your Next LLM Might Not Have A Tokenizer

    Why Your Next LLM Might Not Have A Tokenizer The Tokenizer Has Been a Necessary Evil, but This Radical Approach Shows That It Might Not Be Necessary Anymore. The post Why Your Next LLM Might Not Have A Tokenizer appeared first on Towards Data Science. Moulik Gupta Go to original source

  • Reinforcement Learning from Human Feedback, Explained Simply

    Reinforcement Learning from Human Feedback, Explained Simply The one technique that made ChatGPT so smart The post Reinforcement Learning from Human Feedback, Explained Simply appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • Programming, Not Prompting: A Hands-On Guide to DSPy

    Programming, Not Prompting: A Hands-On Guide to DSPy A practical deep dive into declarative AI programming The post Programming, Not Prompting: A Hands-On Guide to DSPy appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Understanding Application Performance with Roofline Modeling

    Understanding Application Performance with Roofline Modeling A common challenge with calculating an application’s performance is that the real-world performance and theoretical performance can differ. With an ecosystem of products that is growing with high performance needs such as High Performance Computing (HPC), gaming, or in the current landscape – Large Language Models (LLMs), it is…

  • LLM-as-a-Judge: A Practical Guide

    LLM-as-a-Judge: A Practical Guide How to Scale LLM Evaluations Beyond Manual Review The post LLM-as-a-Judge: A Practical Guide appeared first on Towards Data Science. Shuai Guo Go to original source

  • LLaVA on a Budget: Multimodal AI with Limited Resources

    LLaVA on a Budget: Multimodal AI with Limited Resources Let’s get started with multimodality The post LLaVA on a Budget: Multimodal AI with Limited Resources appeared first on Towards Data Science. Marcello Politi Go to original source

  • Build an AI Agent to Explore Your Data Catalog with Natural Language

    Build an AI Agent to Explore Your Data Catalog with Natural Language Leverage LLMs to query your Databricks Data Catalog The post Build an AI Agent to Explore Your Data Catalog with Natural Language appeared first on Towards Data Science. Fabiana Clemente Go to original source

  • What If I had AI in 2018: Rent the Runway Fulfillment Center Optimization

    What If I had AI in 2018: Rent the Runway Fulfillment Center Optimization An LLM in 2018 would not have trivialized a complex project, although it could have enhanced the final solution The post What If I had AI in 2018: Rent the Runway Fulfillment Center Optimization appeared first on Towards Data Science. Hugo Ducruc…

  • How AI Agents “Talk” to Each Other

    How AI Agents “Talk” to Each Other Minimize chaos and maintain inter-agent harmony in your projects The post How AI Agents “Talk” to Each Other appeared first on Towards Data Science. TDS Editors Go to original source

  • Agentic AI 103: Building Multi-Agent Teams

    Agentic AI 103: Building Multi-Agent Teams Build multi-agent teams that can automate tasks and enhance productivity. The post Agentic AI 103: Building Multi-Agent Teams appeared first on Towards Data Science. Gustavo Santos Go to original source

  • Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox

    Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox Not just what you ask, but how you ask it. Practical techniques for prompt engineering that deliver The post Design Smarter Prompts and Boost Your LLM Output: Real Tricks from an AI Engineer’s Toolbox appeared first on Towards Data Science. Ugo Pradère…

  • Can AI Truly Develop a Memory That Adapts Like Ours?

    Can AI Truly Develop a Memory That Adapts Like Ours? Exploring Titans: A new architecture equipping LLMs with human-inspired memory that learns and updates itself during test-time. The post Can AI Truly Develop a Memory That Adapts Like Ours? appeared first on Towards Data Science. Moulik Gupta Go to original source

  • LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries

    LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries Local Large Language Models can convert massive DataFrames to presentable Markdown reports — here’s how. The post LLMs + Pandas: How I Use Generative AI to Generate Pandas DataFrame Summaries appeared first on Towards Data Science. Dario Radečić Go to original source

  • LLM Optimization: LoRA and QLoRA

    LLM Optimization: LoRA and QLoRA Scalable fine-tuning techniques for large language models The post LLM Optimization: LoRA and QLoRA appeared first on Towards Data Science. Vyacheslav Efimov Go to original source

  • GAIA: The LLM Agent Benchmark Everyone’s Talking About

    GAIA: The LLM Agent Benchmark Everyone’s Talking About What practitioners need to know about this LLM agent benchmark The post GAIA: The LLM Agent Benchmark Everyone’s Talking About appeared first on Towards Data Science. Shuai Guo Go to original source

  • From Data to Stories: Code Agents for KPI Narratives

    From Data to Stories: Code Agents for KPI Narratives HuggingFace’s smolagents framework in action The post From Data to Stories: Code Agents for KPI Narratives appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • Tree of Thought Prompting: Teaching LLMs to Think Slowly

    Tree of Thought Prompting: Teaching LLMs to Think Slowly Playing Minesweeper with Augmented Reasoning The post Tree of Thought Prompting: Teaching LLMs to Think Slowly appeared first on Towards Data Science. Shuyang Go to original source

  • Code Agents: The Future of Agentic AI

    Code Agents: The Future of Agentic AI HuggingFace smolagents framework in action The post Code Agents: The Future of Agentic AI appeared first on Towards Data Science. Mariya Mansurova Go to original source

  • How to Evaluate LLMs and Algorithms — The Right Way

    How to Evaluate LLMs and Algorithms — The Right Way Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more. Subscribe today! All the hard work it takes to integrate large language models and powerful algorithms into your workflows can go to waste…

  • Google’s AlphaEvolve: Getting Started with Evolutionary Coding Agents

    Google’s AlphaEvolve: Getting Started with Evolutionary Coding Agents Introduction AlphaEvolve [1] is a promising new coding agent by Google’s DeepMind. Let’s look at what it is and why it is generating hype. Much of the Google paper is on the claim that AlphaEvolve is facilitating novel research through its ability to improve code until it solves…

  • Agentic AI 102: Guardrails and Agent Evaluation

    Agentic AI 102: Guardrails and Agent Evaluation Introduction In the first post of this series (Agentic AI 101: Starting Your Journey Building AI Agents), we talked about the fundamentals of creating AI Agents and introduced concepts like reasoning, memory, and tools. Of course, that first post touched only the surface of this new area of…

  • Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer

    Google’s AlphaEvolve Is Evolving New Algorithms — And It Could Be a Game Changer AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Picture created by the author using various tools including Dall-E3 via ChatGPT. Large Language Models have undeniably revolutionized how many of us approach coding, but they’re often more like a super-powered…

  • Empowering LLMs to Think Deeper by Erasing Thoughts

    Empowering LLMs to Think Deeper by Erasing Thoughts Introduction Recent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called…

  • What My GPT Stylist Taught Me About Prompting Better

    What My GPT Stylist Taught Me About Prompting Better When I built a GPT-powered fashion assistant, I expected runway looks—not memory loss, hallucinations, or semantic déjà vu. But what unfolded became a lesson in how prompting really works—and why LLMs are more like wild animals than tools. This article builds on my previous article on…

  • Real-Time Interactive Sentiment Analysis in Python

    Real-Time Interactive Sentiment Analysis in Python You know what the best part of being an engineer is? You can just build stuff. It’s like a superpower. One rainy afternoon I had this random idea of creating a sentiment visualization of a text input with a smiley face that changes it’s expression base on how positive…

  • Talking to Kids About AI

    Talking to Kids About AI I’ve had the pleasant opportunity recently to be involved with a program called Skype a Scientist, which pairs scientists of various types (biologists, botanists, engineers, computer scientists, etc) with classrooms of kids to talk about our work and answer their questions. I’m pretty familiar with discussing AI and machine learning with…

  • Agentic AI 101: Starting Your Journey Building AI Agents

    Agentic AI 101: Starting Your Journey Building AI Agents Introduction The Artificial Intelligence industry is moving fast. It is impressive and many times overwhelming. I have been studying, learning, and building my foundations in this area of Data Science because I believe that the future of Data Science is strongly correlated with the development of…

  • LLM Evaluations: from Prototype to Production

    LLM Evaluations: from Prototype to Production Evaluation is the cornerstone of any machine learning product. Investing in quality measurement delivers significant returns. Let’s explore the potential business benefits. As management consultant and writer Peter Drucker once said, “If you can’t measure it, you can’t improve it.” Building a robust evaluation system helps you identify areas…

  • An LLM-Based Workflow for Automated Tabular Data Validation 

    An LLM-Based Workflow for Automated Tabular Data Validation  This article is part of a series of articles on automating data cleaning for any tabular dataset: Effortless Spreadsheet Normalisation With LLM You can test the feature described in this article on your own dataset using the CleanMyExcel.io service, which is free and requires no registration. What…

  • The Invisible Revolution: How Vectors Are (Re)defining Business Success

    The Invisible Revolution: How Vectors Are (Re)defining Business Success In a world that focuses more on data, business leaders must understand vector thinking. At first, vectors may appear as complicated as algebra was in school, but they serve as a fundamental building block. Vectors are as essential as algebra for tasks like sharing a bill…

  • Circuit Tracing: A Step Closer to Understanding Large Language Models

    Circuit Tracing: A Step Closer to Understanding Large Language Models Context Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely…

  • AI in Social Research and Polling

    AI in Social Research and Polling This month, I’m going to be discussing a really interesting topic that I came across in a recent draft paper by a professor at the University of Maryland named M. R. Sauter. In the paper, they discuss (among other things) the phenomenon of social scientists and pollsters trying to employ…

  • Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs 

    Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs  Creating efficient prompts for large language models often starts as a simple task… but it doesn’t always stay that way. Initially, following basic best practices seems sufficient: adopt the persona of a specialist, write clear instructions, require a specific response format, and…

  • Are You Still Using LoRA to Fine-Tune Your LLM?

    Are You Still Using LoRA to Fine-Tune Your LLM? LoRA (Low Rank Adaptation – arxiv.org/abs/2106.09685) is a popular technique for fine-tuning Large Language Models (LLMs) on the cheap. But 2024 has seen an explosion of new parameter-efficient fine-tuning techniques, an alphabet soup of LoRA alternatives: SVF, SVFT, MiLoRA, PiSSA, LoRA-XS … And most are based…

  • LLM + RAG: Creating an AI-Powered File Reader Assistant

    LLM + RAG: Creating an AI-Powered File Reader Assistant Introduction AI is everywhere.  It is hard not to interact at least once a day with a Large Language Model (LLM). The chatbots are here to stay. They’re in your apps, they help you write better, they compose emails, they read emails…well, they do a lot.…

  • LLaDA: The Diffusion Model That Could Redefine Language Generation

    LLaDA: The Diffusion Model That Could Redefine Language Generation Introduction What if we could make language models think more like humans? Instead of writing one word at a time, what if they could sketch out their thoughts first, and gradually refine them? This is exactly what Large Language Diffusion Models (LLaDA) introduces: a different approach to…

  • AI Agents from Zero to Hero – Part 1

    AI Agents from Zero to Hero – Part 1 Intro AI Agents are autonomous programs that perform tasks, make decisions, and communicate with others. Normally, they use a set of tools to help complete tasks. In GenAI applications, these Agents process sequential reasoning and can use external tools (like web searches or database queries) when…

  • Tutorial: Semantic Clustering of User Messages with LLM Prompts

    Tutorial: Semantic Clustering of User Messages with LLM Prompts As a Developer Advocate, it’s challenging to keep up with user forum messages and understand the big picture of what users are saying. There’s plenty of valuable content — but how can you quickly spot the key conversations? In this tutorial, I’ll show you an AI…

  • How to Measure the Reliability of a Large Language Model’s Response

    How to Measure the Reliability of a Large Language Model’s Response The basic principle of Large Language Models (LLMs) is very simple: to predict the next word (or token) in a sequence of words based on statistical patterns in their training data. However, this seemingly simple capability turns out to be incredibly sophisticated when it…

  • I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms

    I Tried Making my Own (Bad) LLM Benchmark to Cheat in Escape Rooms Recently, DeepSeek announced their latest model, R1, and article after article came out praising its performance relative to cost, and how the release of such open-source models could genuinely change the course of LLMs forever. That is really exciting! And also, too…

  • Training Large Language Models: From TRPO to GRPO

    Training Large Language Models: From TRPO to GRPO Deepseek has recently made quite a buzz in the AI community, thanks to its impressive performance at relatively low costs. I think this is a perfect opportunity to dive deeper into how Large Language Models (LLMs) are trained. In this article, we will focus on the Reinforcement Learning…

  • Supercharge Your RAG with Multi-Agent Self-RAG

    Supercharge Your RAG with Multi-Agent Self-RAG Introduction Many of us might have tried to build a RAG application and noticed it falls significantly short of addressing real-life needs. Why is that? It’s because many real-world problems require multiple steps of information retrieval and reasoning. We need our agent to perform those as humans normally do,…

  • From Resume to Cover Letter Using AI and LLM, with Python and Streamlit

    From Resume to Cover Letter Using AI and LLM, with Python and Streamlit DISCLAIMER: The idea of doing Cover Letter or even Resume with AI does not obviously start with me. A lot of people have done this before (very successfully) and have built websites and even companies from the idea. This is just a…

  • Improving Agent Systems & AI Reasoning

    Improving Agent Systems & AI Reasoning DeepSeek-R1, OpenAI o1 & o3, Test-Time Compute Scaling, Model Post-Training and the Transition to Reasoning Language Models (RLMs) Image by author and GPT-4o meant to represent DeepSeek and other competitive GenAI model providers Introduction Over the past year generative AI adoption and AI Agent development have skyrocketed. Reports from LangChain…

  • Sparse AutoEncoder: from Superposition to interpretable features

    Sparse AutoEncoder: from Superposition to interpretable features Disentangle features in complex Neural Network with superpositions Complex neural networks, such as Large Language Models (LLMs), suffer quite often from interpretability challenges. One of the most important reasons for such difficulty is superposition — a phenomenon of the neural network having fewer dimensions than the number of features it…

  • How to Implement Guardrails for Your AI Agents with CrewAI

    How to Implement Guardrails for Your AI Agents with CrewAI LLM Agents are non-deterministic by nature: implement proper guardrails for your AI Application. Continue reading on Towards Data Science » Alessandro Romano Go to original source

  • Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems

    Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems How natural systems fundamental laws help explain AI’s unexpected abilities Continue reading on Towards Data Science » Javier Marin Go to original source

  • On a Time Crunch but Still Want to Learn to Develop Multi-Agent AI?

    On a Time Crunch but Still Want to Learn to Develop Multi-Agent AI? These 3 starter projects only take a weekend (and a few cups of coffee, maybe) Continue reading on Towards Data Science » Thuwarakesh Murallie Go to original source

  • How to Evaluate LLM Summarization

    How to Evaluate LLM Summarization A practical and effective guide for evaluating AI summaries Image from Unsplash Summarization is one of the most practical and convenient tasks enabled by LLMs. However, compared to other LLM tasks like question-asking or classification, evaluating LLMs on summarization is far more challenging. And so I myself have neglected evals for…

  • Why Generative-AI Apps’ Quality Often Sucks and What to Do About It

    Why Generative-AI Apps’ Quality Often Sucks and What to Do About It How to get from PoCs to tested high-quality applications in production Image licensed from elements.envato.com, edit by Marcel Müller, 2025 The generative AI hype has rolled through the business world in the past two years. This technology can make business process executions more efficient,…

  • How to Use Pre-Trained Language Models for Regression

    How to Use Pre-Trained Language Models for Regression Why and how to convert mT5 into a regression metric for numerical prediction Continue reading on Towards Data Science » Aden Haussmann Go to original source

  • What Would a Stoic Do? — An AI-Based Decision-Making Model

    What Would a Stoic Do? — An AI-Based Decision-Making Model Using AI to build Marcus Aurelius’ reincarnation Continue reading on Towards Data Science » Pol Marin Go to original source

  • Linearizing Llama

    Linearizing Llama Speeding up Llama: A hybrid approach to attention mechanisms Source: Image by Author (Generated using Gemini 1.5 Flash) In this article, we will see how to replace softmax self-attention in Llama-3.2-1B with hybrid attention combining softmax sliding window and linear attention. This implementation will help us better understand the growing interest in linear attention…

  • Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph

    Building Autonomous Multi-Tool Agents with Gemini 2.0 and LangGraph A practical tutorial with full code examples for building and running multi-tool agents Continue reading on Towards Data Science » Youness Mansar Go to original source

  • Understanding the Evolution of ChatGPT: Part 1—An In-Depth Look at GPT-1 and What Inspired It

    Understanding the Evolution of ChatGPT: Part 1—An In-Depth Look at GPT-1 and What Inspired It Tracing the roots of ChatGPT: GPT-1, the foundation of OpenAI’s LLMs (Image from Unsplash) The GPT (Generative Pre-Training) model family, first introduced by OpenAI in 2018, is another important application of the Transformer architecture. It has since evolved through versions like…

  • AI Agents Hype, Explained — What You Really Need to Know to Get Started

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