In 2026, prompt engineering has evolved into context engineering. Modern models like GPT-5.6 and Claude can now interpret intent from imprecise queries, making complex role-playing preambles less effective. Research from the University of Southern California indicates that assigning roles like 'expert programmer' can actually decrease accuracy on benchmarks, dropping performance from 71.6% to 68%.
OpenAI internal tests for GPT-5.6 show that simplifying system prompts by removing redundant rules improves quality by 10–15% while reducing token consumption by 41–66%. While role-based prompts are less useful for factual tasks, they remain effective for safety and structural constraints, such as increasing correct refusals for harmful requests from 53.2% to 70.9%.
The market for prompt generators has fragmented into various tools, including Taskade, which offers a free tier with 3,000 credits, and PromptHero, which focuses on visual media. Meanwhile, prompt libraries like Awesome ChatGPT Prompts, launched in December 2022, now host thousands of entries. Yandex introduced a Russian-language library in November 2025 featuring over 1,000 prompts across 50 categories.
For effective results, experts recommend a four-block structure: goal, context, format, and boundaries. This approach avoids the 'scaffolding tax'—a term used by OpenAI to describe the performance loss caused by overly prescriptive, step-by-step instructions. Instead of rigid sequences, users are encouraged to define the desired outcome and allow the model to determine the optimal process.
