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Recent research has uncovered some interesting techniques for prompting large language models (LLMs) Prompt strategies aim to activate System 2-like latent knowledge. How can prompt engineers actually implement latent space activation? An even more powerful technique is to prompt the model to brainstorm lists of targeted search queries With the right prompts, extraordinary latent potential can be activated!

When Prompting Stopped Being the Point At some point, I realized I wasn't really writing prompts anymore The best outputs weren't coming from perfectly structured prompts. " prompts. It's not just a better way to prompt a model or automate a task. Prompting is too shallow. Agent is too loaded.

There are six elements that make a prompt work. The shortcut: Task + Context + Format gets you 80% of the way there on most prompts. Drop any of these into a prompt and watch the output change. Save this prompt to your ChatGPT memory: > "Act as a skilled editor. Six prompt components. Ten power codes. One editing prompt. That's the whole cheat sheet. Save it.

Without a prompt. Without you. The engineers who shipped it called it Dreaming on purpose. Instead of waking every prompt, the system wakes every minute. Then every second. Current language models never do this unless prompted. At that point, you stop writing prompts. You stop opening apps. while making coffee — a new direction, a concern, a half-formed idea — and you don’t think of it as a prompt

RouteLLM functions by classifying prompts before they are sent to a large language model. This classification determines which model is best suited for the given prompt, allowing for the use The primary advantage of this approach is that it avoids the need to send every single prompt to a high-cost Classifying and Routing Prompts:    Implement a simple script to classify and route prompts:    from By routing approximately 90% of prompts to less expensive models, you can reduce AI operational costs

Prompts, specifically. I called it the Prompt Potato Gun. Because of course I did. Dropped it into the Glaze prompt. Hit plan. Watched it think for a couple of minutes. actually get the most use out of this — people hammering LLMs all day, people who copy the same system prompt The Prompt Potato Gun was hardware because in 2024, software was still too slow and awkward to deliver

You’ve got your prompts. Your secret sauce. Maybe you’ve spent hours refining them. Shared Prompt Libraries Create a central place where your team can drop and remix great prompts.Your Your AI team (a.k.a. everyone ) should have prompt recipes. 2. #AIForTeams, #FutureOfWork, #CollaborativeAI, #TeamProductivity, #PromptSharing, #DigitalTransformation

A Three-Part Series to Blow Your Mind (Accidentally) You ever trip over a prompt and land in a whole What started as prompt design turned into self-discovery .

full-blown quest to create the ultimate tool for quick access to common keyboard shortcuts and, of course, prompt The Prompt Potato Gun Project: A Bluetooth Macro Keyboard At the heart of this project is the Xiao ESP32 Each of these buttons is programmed to paste blocks of preset text (or prompts, if you’re being literal , but it also has a "config mode" accessible through Wi-Fi, where you can customize or change those prompts EasyEDA—soon, designing and building custom electronics could be as simple as typing, or launching 😂 a prompt

The very first prompt that launched the project wasn't some 10-page product specification or meticulously engineered prompt library. One paragraph prompt. A few hours of focused building.

across rows — alternating between the two dimensions — which is how a standard transformer would read a prompt , except the prompt is your spreadsheet. Same mechanism an LLM uses to read a few-shot prompt. Except here the prompt is tabular. The historical examples and target rows are supplied together as a unified prompt and processed together

Same prompt, same surface reasoning, different silent variable underneath, different answer. global workspace theory in neuroscience, where a small fraction of everything happening in a brain gets promoted activations that track with concepts like fear or calm, scaling with the severity of a scenario in a prompt sit with: they compared a baseline model and a deliberately misaligned model given the same coding prompt