Category: Llm Applications
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How to Create Production-Ready Code with Claude Code
How to Create Production-Ready Code with Claude Code Learn how to write robust code with coding agents. The post How to Create Production-Ready Code with Claude Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Detecting and Editing Visual Objects with Gemini
Detecting and Editing Visual Objects with Gemini A practical guide to identifying, restoring, and transforming elements within your images The post Detecting and Editing Visual Objects with Gemini appeared first on Towards Data Science. Laurent Picard Go to original source
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Build Effective Internal Tooling with Claude Code
Build Effective Internal Tooling with Claude Code Use Claude Code to quickly build completely personalized applications The post Build Effective Internal Tooling with Claude Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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The Reality of Vibe Coding: AI Agents and the Security Debt Crisis
The Reality of Vibe Coding: AI Agents and the Security Debt Crisis Why optimizing for speed over safety is leaving applications vulnerable, and how to fix it. The post The Reality of Vibe Coding: AI Agents and the Security Debt Crisis appeared first on Towards Data Science. Reya Vir Go to original source
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An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI
An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI The guide to automated improvement of scientific and industrial repositories using open-source AI agents The post An End-to-End Guide to Beautifying Your Open-Source Repo with Agentic AI appeared first on Towards Data Science. Nikolay Nikitin Go to original source
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How to Personalize Claude Code
How to Personalize Claude Code Learn how to get more out of Claude code by giving it access to more information. The post How to Personalize Claude Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Work Effectively with Frontend and Backend Code
How to Work Effectively with Frontend and Backend Code Learn how to be an effective full-stack engineer with Claude Code The post How to Work Effectively with Frontend and Backend Code appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Build Your Own Custom LLM Memory Layer from Scratch
How to Build Your Own Custom LLM Memory Layer from Scratch Step-by-step guide to building autonomous memory retrieval systems The post How to Build Your Own Custom LLM Memory Layer from Scratch appeared first on Towards Data Science. Avishek Biswas Go to original source
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How to Apply Agentic Coding to Solve Problems
How to Apply Agentic Coding to Solve Problems Learn how to efficiently solve problems with coding agents The post How to Apply Agentic Coding to Solve Problems appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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The Unbearable Lightness of Coding
The Unbearable Lightness of Coding Confessions of a vibe coder The post The Unbearable Lightness of Coding appeared first on Towards Data Science. Elena Jolkver Go to original source
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Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors
Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird delimiters. The post Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors appeared first on Towards Data Science. Benjamin Nweke Go to original source
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A Geometric Method to Spot Hallucinations Without an LLM Judge
A Geometric Method to Spot Hallucinations Without an LLM Judge Imagine a flock of birds in flight. There’s no leader. No central command. Each bird aligns with its neighbors—matching direction, adjusting speed, maintaining coherence through purely local coordination. The result is global order emerging from local consistency. Now imagine one bird flying with the same…
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Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries
Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts of the NLP toolkit. The post Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries appeared first on Towards Data Science. Petr Koráb Go to…
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An introduction to AWS Bedrock
An introduction to AWS Bedrock The how, why, what and where of Amazon’s LLM access layer The post An introduction to AWS Bedrock appeared first on Towards Data Science. Thomas Reid Go to original source
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How to Maximize Claude Code Effectiveness
How to Maximize Claude Code Effectiveness Learn how to get the most out of agentic coding The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Leverage Slash Commands to Code Effectively
How to Leverage Slash Commands to Code Effectively Learn how I utilize slash commands to be a more efficient engineer The post How to Leverage Slash Commands to Code Effectively appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows
HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows How approximate vector search silently degrades Recall—and what to do about It The post HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows appeared first on Towards Data Science. Partha Sarkar Go to original source
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Measuring What Matters with NeMo Agent Toolkit
Measuring What Matters with NeMo Agent Toolkit A practical guide to observability, evaluations, and model comparisons The post Measuring What Matters with NeMo Agent Toolkit appeared first on Towards Data Science. Mariya Mansurova Go to original source
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Prompt Engineering vs RAG for Editing Resumes
Prompt Engineering vs RAG for Editing Resumes Running a code-free comparison in Azure The post Prompt Engineering vs RAG for Editing Resumes appeared first on Towards Data Science. Robert Etter Go to original source
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How to Keep MCPs Useful in Agentic Pipelines
How to Keep MCPs Useful in Agentic Pipelines Check the tools your LLM uses before replacing it with just a more powerful model The post How to Keep MCPs Useful in Agentic Pipelines appeared first on Towards Data Science. Roman S Go to original source
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ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI
ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI For the last couple of years, a lot of the conversation around AI has revolved around a single, deceptively simple question: Which model is the best? But the next question was always, the best for what? The best for reasoning? Writing? Coding? Or…
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Tools for Your LLM: a Deep Dive into MCP
Tools for Your LLM: a Deep Dive into MCP MCP is a key enabler into turning your LLM into an agent by providing it with tools to retrieve real-time information or perform actions. In this deep dive we cover how MCP works, when to use it, and what to watch out for. The post Tools…
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4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
4 Ways to Supercharge Your Data Science Workflow with Google AI Studio With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker. The post 4 Ways to Supercharge Your Data Science Workflow with Google AI Studio appeared first on Towards Data Science. Shuai Guo Go to…
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3 Techniques to Effectively Utilize AI Agents for Coding
3 Techniques to Effectively Utilize AI Agents for Coding Learn how to be an effective engineer with coding agents The post 3 Techniques to Effectively Utilize AI Agents for Coding appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Lessons Learned from Upgrading to LangChain 1.0 in Production
Lessons Learned from Upgrading to LangChain 1.0 in Production What worked, what broke, and why I did it The post Lessons Learned from Upgrading to LangChain 1.0 in Production appeared first on Towards Data Science. Clara Chong Go to original source
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How to Increase Coding Iteration Speed
How to Increase Coding Iteration Speed Learn how to become a more efficient programmer with local testing The post How to Increase Coding Iteration Speed appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Maximize Agentic Memory for Continual Learning
How to Maximize Agentic Memory for Continual Learning Learn how to become an effective engineer with continual learning LLMs The post How to Maximize Agentic Memory for Continual Learning appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot
Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access. The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first…
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How to Develop AI-Powered Solutions, Accelerated by AI
How to Develop AI-Powered Solutions, Accelerated by AI From idea to impact : using AI as your accelerating copilot The post How to Develop AI-Powered Solutions, Accelerated by AI appeared first on Towards Data Science. Anna Via Go to original source
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The AI Bubble Will Pop — And Why That Doesn’t Matter
The AI Bubble Will Pop — And Why That Doesn’t Matter How history’s biggest tech bubble explains where AI is headed next The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science. Michael Malin Go to original source
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The Architecture Behind Web Search in AI Chatbots
The Architecture Behind Web Search in AI Chatbots And what this means for generative engine optimization (GEO) The post The Architecture Behind Web Search in AI Chatbots appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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How to Turn Your LLM Prototype into a Production-Ready System
How to Turn Your LLM Prototype into a Production-Ready System The most famous applications of LLMs are the ones that I like to call the “wow effect LLMs.” There are plenty of viral LinkedIn posts about them, and they all sound like this: “I built [x] that does [y] in [z] minutes using AI.” Where:…
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Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It
Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It A real-world analysis of why CrewAI’s hierarchical orchestration misfires—and a practical fix you can implement today. The post Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It appeared first on Towards Data Science. Partha Sarkar Go to original source
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Ten Lessons of Building LLM Applications for Engineers
Ten Lessons of Building LLM Applications for Engineers Practical field notes on workflows, structure, and evaluation from two years of building with engineering domain experts. The post Ten Lessons of Building LLM Applications for Engineers appeared first on Towards Data Science. Shuai Guo Go to original source
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How to Create Professional Articles with LaTeX in Cursor
How to Create Professional Articles with LaTeX in Cursor Learn how to rapidly create professional articles and presentations with LaTeX in Cursor The post How to Create Professional Articles with LaTeX in Cursor appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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A Hands-On Guide to Anthropic’s New Structured Output Capabilities
A Hands-On Guide to Anthropic’s New Structured Output Capabilities A developer’s guide to perfect JSON and typed outputs from Claude Sonnet 4.5 and Opus 4.1 The post A Hands-On Guide to Anthropic’s New Structured Output Capabilities appeared first on Towards Data Science. Thomas Reid Go to original source
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How to Build Your Own Agentic AI System Using CrewAI
How to Build Your Own Agentic AI System Using CrewAI This article demonstrates how to develop your own Agentic AI system using CrewAI framework. By orchestrating specialized agents with distinct roles and tools, we implement a multi-agent team that is capable of generating optimized content for different social media platforms. The post How to Build…
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How to Use GPT-5 Effectively
How to Use GPT-5 Effectively Learn about GPT-5’s features and settings, and how to optimally apply them to your use case The post How to Use GPT-5 Effectively appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Apply Vision Language Models to Long Documents
How to Apply Vision Language Models to Long Documents Learn how to apply powerful VLMs for long context document understanding tasks The post How to Apply Vision Language Models to Long Documents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance
4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance Learn how to greatly improve the performance of your LLM application The post 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Keep AI Costs Under Control
How to Keep AI Costs Under Control Lessons from Scaling LLMs The post How to Keep AI Costs Under Control appeared first on Towards Data Science. Asaf Liveanu Go to original source
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Implementing DRIFT Search with Neo4j and LlamaIndex
Implementing DRIFT Search with Neo4j and LlamaIndex Combining global and local search to get the most accurate response The post Implementing DRIFT Search with Neo4j and LlamaIndex appeared first on Towards Data Science. Tomaz Bratanic Go to original source
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How to Build Guardrails for Effective Agents
How to Build Guardrails for Effective Agents Learn how to set up effective guardrails to enforce desired behaviour from your agents The post How to Build Guardrails for Effective Agents appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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This Puzzle Shows Just How Far LLMs Have Progressed in a Little Over a Year
This Puzzle Shows Just How Far LLMs Have Progressed in a Little Over a Year What took GPT-4o 2 hours to solve, Sonnet 4.5 does in 5 seconds The post This Puzzle Shows Just How Far LLMs Have Progressed in a Little Over a Year appeared first on Towards Data Science. Thomas Reid Go to original source
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How To Build Effective Technical Guardrails for AI Applications
How To Build Effective Technical Guardrails for AI Applications Exploring the most practical guardrails to implement at ground level The post How To Build Effective Technical Guardrails for AI Applications appeared first on Towards Data Science. Nidhin Karunakaran Ponon Go to original source
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MCP in Practice
MCP in Practice Mapping power, concentration, and usage in the emerging AI developer ecosystem The post MCP in Practice appeared first on Towards Data Science. Sruly Rosenblat Go to original source
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Notes on LLM Evaluation
Notes on LLM Evaluation A practical, step-by-step guide to building an evaluation pipeline for a real-world AI application The post Notes on LLM Evaluation appeared first on Towards Data Science. Felipe Adachi Go to original source
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Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output
Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output A hands-on example of building a time-series anomaly detection system entirely through visualization and prompting The post Building LLM Apps That Can See, Think, and Integrate: Using o3 with Multimodal Input and Structured Output appeared first on Towards…
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How I Built and Deployed an App in 2 days with Lovable, Supabase, and Netlify
How I Built and Deployed an App in 2 days with Lovable, Supabase, and Netlify All ideas can be turned into action in a matter of time now. The post How I Built and Deployed an App in 2 days with Lovable, Supabase, and Netlify appeared first on Towards Data Science. Soner Yıldırım Go to…
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My Experiments with NotebookLM for Teaching
My Experiments with NotebookLM for Teaching Exploring NotebookLM as a teaching companion The post My Experiments with NotebookLM for Teaching appeared first on Towards Data Science. Parul Pandey Go to original source
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Why Task-Based Evaluations Matter
Why Task-Based Evaluations Matter This article is adapted from a lecture series I gave at Deeplearn 2025: From Prototype to Production: Evaluation Strategies for Agentic Applications. Task-based evaluations, which measure an AI system’s performance in use-case-specific, real-world settings, are underadopted and understudied. There is still an outsized focus in AI literature on foundation model benchmarks.…
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LangGraph 201: Adding Human Oversight to Your Deep Research Agent
LangGraph 201: Adding Human Oversight to Your Deep Research Agent Losing control of your AI agent in the middle of the workflow is a common pain point. If you have built your own agentic applications, you’ve most likely already seen this happen. While LLMs nowadays are incredibly capable, they’re still not quite there yet to…
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Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows
Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows A guide to building modular workflows for structured intelligence The post Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows appeared first on Towards Data Science. Subha Ganapathi Go to original source
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AI Operations Under the Hood: Challenges and Best Practices
AI Operations Under the Hood: Challenges and Best Practices Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation, and systematic validation The post AI Operations Under the Hood: Challenges and Best Practices appeared first on Towards Data Science. Erika G. Gonçalves Go to original source
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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
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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
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Crafting a Custom Voice Assistant with Perplexity
Crafting a Custom Voice Assistant with Perplexity How to build a fully functional, hands-free voice assistant on a Raspberry Pi The post Crafting a Custom Voice Assistant with Perplexity appeared first on Towards Data Science. Deepak Krishnamurthy Go to original source
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Unlocking Multimodal Video Transcription with Gemini
Unlocking Multimodal Video Transcription with Gemini Explore how to transcribe videos with speaker identification in a single prompt The post Unlocking Multimodal Video Transcription with Gemini appeared first on Towards Data Science. Laurent Picard Go to original source
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“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
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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
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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
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Context Engineering — A Comprehensive Hands-On Tutorial with DSPy
Context Engineering — A Comprehensive Hands-On Tutorial with DSPy Let’s dissect the art and science of context engineering, one module at a time! The post Context Engineering — A Comprehensive Hands-On Tutorial with DSPy appeared first on Towards Data Science. Avishek Biswas Go to original source
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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
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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
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Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide
Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide Learn how to create AI Agents using the OpenAI Agents SDK to automate Jira ticket creation from a meeting transcript. The post Automating Ticket Creation in Jira With the OpenAI Agents SDK: A Step-by-Step Guide appeared first on Towards Data Science. Juan…
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Advanced Topic Modeling with LLMs
Advanced Topic Modeling with LLMs A deep dive into topic modeling by leveraging representation models and generative AI with BERTopic The post Advanced Topic Modeling with LLMs appeared first on Towards Data Science. Alex Davis Go to original source
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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
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Use OpenAI Whisper for Automated Transcriptions
Use OpenAI Whisper for Automated Transcriptions Streamline your computer interactions using OpenAI’s Whisper model The post Use OpenAI Whisper for Automated Transcriptions appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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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
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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
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Build and Query Knowledge Graphs with LLMs
Build and Query Knowledge Graphs with LLMs Knowledge Graphs are relevant A Knowledge Graph could be defined as a structured representation of information that connects concepts, entities, and their relationships in a way that mimics human understanding. It is often used to organise and integrate data from various sources, enabling machines to reason, infer, and retrieve relevant…
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Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit
Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit In this post, I’ll show you step by step how to build and deploy a chat powered with LLM — Gemini — in Streamlit and monitor the API usage on Google Cloud Console. Streamlit is a Python framework that makes it super easy to turn your…
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A Step-By-Step Guide To Powering Your Application With LLMs
A Step-By-Step Guide To Powering Your Application With LLMs You might be wondering whether GenAI is just hype or external noise. I also thought this was hype, and I could sit this one out until the dust cleared. Oh, boy, was I wrong. GenAI has real-world applications. It also generates revenue for companies, so we expect…
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Retrieval Augmented Generation (RAG) — An Introduction
Retrieval Augmented Generation (RAG) — An Introduction The model hallucinated! It was giving me OK answers and then it just started hallucinating. We’ve all heard or experienced it. Natural Language Generation models can sometimes hallucinate, i.e., they start generating text that is not quite accurate for the prompt provided. In layman’s terms, they start making…
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Agentic AI: Single vs Multi-Agent Systems
Agentic AI: Single vs Multi-Agent Systems We’ve seen this shift the last few years from building rigid programming systems to natural language-driven workflows, all made possible with more advanced large language models. One of the interesting areas into these Agentic Ai systems is the difference between building a single versus multi-agent workflow, or perhaps the…
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The Impact of GenAI and Its Implications for Data Scientists
The Impact of GenAI and Its Implications for Data Scientists GenAI systems affect how we work. This general notion is well known. However, we are still unaware of the exact impact of GenAI. For example, how much do these tools affect our work? Do they have a larger impact on certain tasks? What does this…
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Deep Research by OpenAI: A Practical Test of AI-Powered Literature Review
Deep Research by OpenAI: A Practical Test of AI-Powered Literature Review “Conduct a comprehensive literature review on the state-of-the-art in Machine Learning and energy consumption. […]” With this prompt, I tested the new Deep Research function, which has been integrated into the OpenAI o3 reasoning model since the end of February — and conducted a state-of-the-art literature…