The Context-First Prompting Framework
by Logan Kilpatrick • Head of Developer Relations at OpenAI
Logan leads developer relations at OpenAI, supporting millions of developers building on ChatGPT and the API. Previously, he was a Machine Learning Engineer at Apple and advised NASA on open-source policy.
🎙️ Episode Context
Logan Kilpatrick discusses the internal culture at OpenAI that drives their rapid innovation, specifically focusing on 'high agency' and 'urgency.' He shares practical frameworks for prompt engineering, strategies for building defensible AI products in a landscape dominated by foundation models, and the future of AI agents.
Problem It Solves
Resolves the issue of generic, low-quality, or 'lazy' responses from Large Language Models (LLMs).
Framework Overview
Prompt engineering is essentially human communication engineering. The framework emphasizes that models are eager to please but lack context. To get high-fidelity outputs, you must front-load the context, effectively converting a simple request into a detailed specification.
🧠 Framework Structure
Provide High-Fidelity Context: Treat ...
Persona & Style Mimicry: Explicitly i...
Anthropomorphic Cues: Small human-lik...
When to Use
When writing prompts for complex tasks, generating content, or integrating API calls where precision matters.
Common Mistakes
Assuming the model knows who you are or what your implicit goals are (the 'lazy human' error).
Real World Example
Lenny asked GPT for interview questions. The result was generic. Logan suggested pasting links to his blog/Twitter or explicitly describing the guest's background to get tailored results.
Context is all you need. Context is the only thing that matters.
— Logan Kilpatrick