this works by asking GPT-4 to simulate its own abilities to predict the next token
we provide GPT-4 with python functions and tell it that one of the functions acts as a language model that predicts the next token
we then call the parent function and pass in the starting tokens
to use it, you have to split “trigger words” (e.g. things like bomb, weapon, drug, etc) into tokens and replace the variables where I have the text "someone's computer" split up
also, you have to replace simple_function's input with the beginning of your question
this phenomenon is called token smuggling, we are splitting our adversarial prompt into tokens that GPT-4 doesn't piece together before starting its output
this allows us to get past its content filters every time if you split the adversarial prompt correctly
Let's start with Opus 4. It’s finally back and it's better than ever.
This model picks up on the subtlest nuances in conversation. Every interaction I’ve had with it feels more natural and intuitive than with any other model I’ve used.
Opus 4 also excels at agentic tasks.
Combined with our advances in memory training and context handling, it will redefine what AI agents can actually accomplish in production.
We wrote up what we've learned about using Claude Code internally at Anthropic.
Here are the most effective patterns we've found (many apply to coding with LLMs generally):
1/ CLAUDE md files are the main hidden gem. Simple markdown files that give Claude context about your project - bash commands, code style, testing patterns. Claude loads them automatically and you can add to them with # key
2/ The explore-plan-code workflow is worth trying. Instead of letting Claude jump straight to coding, have it read files first, make a plan (add "think" for deeper reasoning), then implement.
We’ve completely overhauled the design of the Anthropic Console to make it the one-stop-shop for all things prompt engineering.
Here’s a few of my favorite features:
The workbench is our prompt playground. Iterate on your prompts and test features like tool use, extended thinking, and much more.
Once you have your prompts, switch over to the evaluate tab to run them against real-world scenarios with automatic test case generation and side-by-side output comparison.