Prompt engineering techniques and copy-paste prompts that work.
Thirty copy-paste ChatGPT prompts for teachers: lesson plans, differentiation, rubrics, feedback on student work and family emails. Built on OpenAI Academy's teacher guides, with a privacy note and a check to run before class. Prompts untested; review every output.
OpenAI plans to retire custom GPTs on Dec 11, 2026 and move them to plugins. Here is what transfers, what does not, the five migration steps, and a tested prompt for turning old GPT instructions into a plugin brief, as of October 2026.
Thirty copy-paste ChatGPT prompts for tailoring a resume, rewriting bullets, and writing cover letters without invented facts. Based on OpenAI's Job Search Playbook and OpenAI Academy's job seeker prompt pack. Prompts untested; check every output.
A practical vibe-coding workflow for Claude: define the term (Karpathy, Feb 2025), set up Projects or Claude Code with CLAUDE.md, use Anthropic's XML and system-prompt guidance, then run a step-by-step loop with copy-paste prompts. As of October 2026. Prompts untested.
50 copy-paste ChatGPT prompts for everyday work: email, meetings, planning, analysis, research, spreadsheets, managing a team, and career. Each follows the task, context, and output structure from OpenAI's own prompting guide. Untested templates; fill in the brackets.
A practical beginner guide to writing clearer prompts for ChatGPT, Claude, Gemini, and Grok. Built from OpenAI, Anthropic, and Google's own prompting docs: scope one job, add context, specify format, show examples, say what to do, then iterate.
Production AI applications are rapidly moving away from hardcoded prompt strings and manual trial-and-error tweaking. As language model systems expand into multi-stage pipelines, retrieval-augmented generation (RAG) graphs, and multi-agent loops, manual prompt adjustments fail to scale. Changing a system prompt or upstream retrieval format frequently degrades downstream extraction, reasoning, or tool-calling performance. To resolve this fragility, the industry is adopting declarative prompt opt
In-context learning (ICL) remains one of the most practical mechanisms for steering large language models on specialized tasks, structured output parsing, domain-specific classification, and API tool calling. While zero-shot prompts rely entirely on the model's parametric memory, few-shot prompting provides concrete input-output demonstrations that anchor the model's generation trajectory. In enterprise production environments, however, static few-shot prompting quickly hits operational limits.
Manual prompt engineering remains one of the largest sources of technical debt in modern LLM applications. Teams routinely spend weeks hand-crafting multi-paragraph system prompts, hardcoding few-shot examples, and tweaking phrasing to extract reliable outputs from specific model checkpoints. When the underlying model is upgraded, migrated to an open-weight alternative, or integrated into a multi-step pipeline, these hand-crafted strings break, requiring another cycle of trial-and-error adjustme
Boris Cherny, the creator and head of Claude Code at Anthropic, said in a recent interview that prompt engineering matters far less than most users think, and that the real skill is giving Claude hard tasks with a way to verify its own work. Speaking with Y Combinator's Diana Hu, Cherny described an internal experiment where an engineer gave Claude Code access to OpenCV, an open-source computer vision library, and asked it to draw images. Claude did, despite having no obvious prior capability f