Let the Model Write the Prompt

Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with DSPy | Towards Data Science

Applying machine learning methodology to prompt building

Cheatsheet - DSPy

The framework for programming—rather than prompting—language models.

How to Make Small Language Models Outperform Large Language Models Using DSPy!

How a 3B Language Model Surpasses an 8B Counterpart with DSPy? “In an era where language models (LMs) are revolutionising countless tasks, their potential is only as powerful as we interpret …

Introducing dtoolAI — dtoolAI 0.1.0 documentation

dtoolAI is a Python library to make reproducible AI model training and use easier. The dtoolAI package provides:

The intent pipeline: why most prompt guides miss how people actually use AI

Most prompt guides assume that people interact with AI by carefully authoring prompts. In practice, that is rarely how AI is used. Most…

What Is DSPy? How It Works, Use Cases, and Resources

DSPy is an open-source Python framework that allows developers to build language model applications using modular and declarative programming instead of relying on one-off prompting techniques.

DSPy Notebook - Ax LLM Framework

Import AI 455: AI systems are about to start building themselves.

The first step towards recursive self improvement

Inside ChatGPT: How AI chatbots work

Large language models like ChatGPT use a complicated series of equations to understand and respond to your prompts. Here’s a look inside the system.

AI 2027

A research-backed AI scenario forecast.

Stories | Cyrus

Insights, tutorials, and case studies on AI-powered development workflows. Learn how teams use Cyrus with Linear and Claude Code to ship 20x faster.

Import AI 466: The bitter lesson for robotics, AIs complete week-long programming tasks; and OpenAI's accidental AI hacker

The warning shots will continue until civilization wakes up

Systems programming the model

This paper examines the status of the language model object in generative AI, arguing that what we call a ‘model’ is inseparable from the systems deploying it. I first theorize how these objects emerge from systems-level interactions between trained artifacts, prompting mechanisms, and sampling methods, drawing on the philosophy of digital objects as well as software studies to show how models gain their objective character. Such interactions converge on programming, not prompting, language models, and I illustrate how critical code studies can therefore track these dynamics. In an overview of language model programming approaches, I discuss how prompt and program converge, demonstrating how this confluence tends toward the production of new feedback loops wherein models become models of and for themselves. Understanding these feedback loops is essential in view of recent efforts to infrastructuralize AI, in which multiple models cascade into compound systems that abstract toward a unified model of models. Thus the need, I argue, for a systems-level view that can address this new order of abstraction and complexity by identifying where and how the model emerges from the system.