Source-backed, reproducible AI prompts and workflows published or updated within the past three months.
Factored Chain-of-Verification (CoVe) is a multi-step prompting architecture that mitigates LLM hallucinations by auditing initial drafts in isolated contexts. This guide presents a practical prompt template and four-phase workflow to ensure factual reliability.
Skeleton-of-Thought (SoT) is a two-phase prompting strategy that decouples structural outline planning from detailed section expansion. By executing parallel API calls for each outline section, SoT reduces overall generation latency by up to 2.39x.
Major AI vendors in mid-2026 advocate replacing step-by-step procedural scripting with outcome-first prompt architecture. This workflow slashes token costs while improving overall evaluation scores.
Prompt engineering has evolved into context engineering and loop engineering, shifting the focus from wording micromanagement to context curation and autonomous execution loops. This guide outlines a production-grade workflow based on modern framework research.
Rooted in the 2026 Loop Engineering methodology, this workflow establishes an automated verification loop for AI agents. It leverages isolated sandboxes and rubric-based feedback to enable continuous self-correction.
Modularization-of-Thought (MoT) replaces linear Chain-of-Thought with a hierarchical reasoning framework, organizing problem solving into structured graphs before generating code. This guide details the MoT prompt scaffold and execution workflow designed to boost code generation Pass@1 accuracy without expensive search loops.
Google DeepMind introduced Visual Prompt Engineering (VIPE), expanding prompt engineering from text to visual transformations. This automated three-stage workflow significantly enhances physical and spatial reasoning accuracy in video foundation models.
Metacognitive prompting enforces explicit cognitive self-monitoring, guiding foundation models to evaluate uncertainty and knowledge limits before delivering conclusions. This article outlines a structured XML prompt template and multi-step workflow designed to minimize hallucination and overconfidence bias.
Only source-checked, reusable prompts and workflows with clear variables are published here.