mailitics

Category: Prompt Engineering

  • Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes

    Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes How much of your AI agent’s output is real data versus confident guesswork? The post Prompt Fidelity: Measuring How Much of Your Intent an AI Agent Actually Executes appeared first on Towards Data Science. James Barney Go to original source

  • Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found

    Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found How prompt engineering has evolved, examined scientifically; and implications for the future of conversational AI tools The post Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research…

  • TDS Newsletter: Beyond Prompt Engineering: The New Frontiers of LLM Optimization

    TDS Newsletter: Beyond Prompt Engineering: The New Frontiers of LLM Optimization Let’s zoom in on recent approaches that push AI-powered workflows to the next level The post TDS Newsletter: Beyond Prompt Engineering: The New Frontiers of LLM Optimization appeared first on Towards Data Science. TDS Editors Go to original source

  • Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example

    Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI’s GPT 5.2 The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science. Vincent Koc Go to…

  • 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

  • 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:…

  • LLM-Powered Time-Series Analysis

    LLM-Powered Time-Series Analysis Part 2: Prompts for Advanced Model Development The post LLM-Powered Time-Series Analysis appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Prompt Engineering for Time-Series Analysis with Large Language Models

    Prompt Engineering for Time-Series Analysis with Large Language Models Part 1: Prompts for Core Strategies in Time-Series The post Prompt Engineering for Time-Series Analysis with Large Language Models appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Generative AI Myths, Busted: An Engineers’s Quick Guide

    Generative AI Myths, Busted: An Engineers’s Quick Guide A super simple and quick guide to how generative AI works, the myths around it, and why it won’t replace engineers anytime soon. The post Generative AI Myths, Busted: An Engineers’s Quick Guide appeared first on Towards Data Science. Amy Ma Go to original source

  • Building Research Agents for Tech Insights

    Building Research Agents for Tech Insights Using a controlled workflow, unique data & prompt chaining The post Building Research Agents for Tech Insights appeared first on Towards Data Science. Ida Silfverskiöld Go to original source

  • 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

  • Tool Masking: The Layer MCP Forgot

    Tool Masking: The Layer MCP Forgot Tool masking for AI improves AI agents: shape MCP tool surfaces to cut tokens and errors, boost speed and reliability. Start prompt engineering your tools The post Tool Masking: The Layer MCP Forgot appeared first on Towards Data Science. Frank Wittkampf Go to original source

  • Why Your Prompts Don’t Belong in Git

    Why Your Prompts Don’t Belong in Git The hidden cost of storing prompts in your source code The post Why Your Prompts Don’t Belong in Git appeared first on Towards Data Science. Giorgos Myrianthous Go to original source

  • Systematic LLM Prompt Engineering Using DSPy Optimization

    Systematic LLM Prompt Engineering Using DSPy Optimization This article is a journey into the fascinating and rapidly evolving science of LLM prompt iteration, which is a fundamental part of Large Language Model Operations (LLMOPs). We’ll use the example of generating customer service responses with a real-world dataset to show how both generator and LLM-judge prompts…

  • Advanced Prompt Engineering for Data Science Projects

    Advanced Prompt Engineering for Data Science Projects Part 2: Prompt Engineering for Features, Modeling, and Evaluation The post Advanced Prompt Engineering for Data Science Projects appeared first on Towards Data Science. Sara Nobrega Go to original source

  • How Your Prompts Lead AI Astray

    How Your Prompts Lead AI Astray Practical tips to recognise and avoid prompt bias. The post How Your Prompts Lead AI Astray appeared first on Towards Data Science. Daphne de Klerk Go to original source

  • Declarative and Imperative Prompt Engineering for Generative AI

    Declarative and Imperative Prompt Engineering for Generative AI Conceptual overview and practical considerations The post Declarative and Imperative Prompt Engineering for Generative AI appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • 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

  • 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

  • How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes

    How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes When numbers lie — and your metrics mislead you The post How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes appeared first on Towards Data Science. Subha Ganapathi Go to original source

  • Recap of all types of LLM Agents

    Recap of all types of LLM Agents Regular, ReAct, Chain-of-Thought, Reflexion, ToT, GoT, PoT The post Recap of all types of LLM Agents appeared first on Towards Data Science. Mauro Di Pietro Go to original source

  • Become a Better Data Scientist with These Prompt Engineering Tips and Tricks

    Become a Better Data Scientist with These Prompt Engineering Tips and Tricks Part 1: prompt engineering for planning, cleaning, and EDA The post Become a Better Data Scientist with These Prompt Engineering Tips and Tricks appeared first on Towards Data Science. Sara Nobrega 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…

  • AI Agents from Zero to Hero — Part 2

    AI Agents from Zero to Hero — Part 2 Intro In Part 1 of this tutorial series, we introduced AI Agents, autonomous programs that perform tasks, make decisions, and communicate with others.  Agents perform actions through Tools. It might happen that a Tool doesn’t work on the first try, or that multiple Tools must be…

  • 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…

  • Generative AI Is Declarative

    Generative AI Is Declarative ChatGPT launched in 2022 and kicked off the Generative Ai boom. In the two years since, academics, technologists, and armchair experts have written libraries worth of articles on the technical underpinnings of generative AI and about the potential capabilities of both current and future generative AI models. Surprisingly little has been…

  • Enhancing RAG: Beyond Vanilla Approaches

    Enhancing RAG: Beyond Vanilla Approaches Retrieval-Augmented Generation (RAG) is a powerful technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This article explores the limitations of a vanilla RAG setup and introduces advanced techniques to enhance its accuracy and…

  • 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…