This new, dead simple prompt technique boosts accuracy on LLMs by up to 76% on non-reasoning tasks

Most modern LLMs are trained as "causal" language models. This means they process text strictly from left to right. When the ...
Digital Journal

The pitfalls of prompt overloading: Why more tokens mean less accuracy in LLMs

Opinions expressed by Digital Journal contributors are their own. As LLMs like OpenAI’s GPT-4 continue to showcase remarkable abilities in generating human-like text, recent research has shed light on ...
Forbes

Beyond Prompt Engineering: The Rise Of Cost-Effective Context Engineering

Sr. Director of Product at Aisera, Jigar brings 15+ years in enterprise AI, GenAI innovation, agentic automation and product-led growth. Enterprise AI is in the midst of a fundamental shift. The early ...

The Hidden AI Cost Few Leaders Track: How Typos Inflate Your LLM Spend

It sounds trivial, almost too silly to be a line item on a CFO’s dashboard. But in a usage-metered world, sloppy typing is a ...
Ars Technica

Why AI language models choke on too much text

Large language models represent text using tokens, each of which is a few characters. Short words are represented by a single token (like “the” or “it”), whereas larger words may be represented by ...