TasteSkill v2 introduces a six-step workflow where AI coding agents self-critique generated web pages in real browsers, use curated real images from open archives, and iterate up to three times to eliminate generic 'AI slop' aesthetics, producing polished, art-directed frontend designs without API keys.
The article explains why AI agents often produce unreliable answers after a tool call finishes, identifies the missing observation layer as the quality‑check station, and details how to format, classify, grade errors, perform self‑critique, enforce a Definition of Done, and prevent loops or premature termination.
The article explains four reusable prompt‑engineering frameworks—role‑play, chain‑of‑thought, few‑shot, and self‑critique—showing how precise role definitions, step‑by‑step reasoning, boundary examples, and iterative review can dramatically improve AI output quality.
Google DeepMind's new "Intrinsic Self‑Critique" method lets large language models iteratively self‑evaluate and rewrite their plans, raising Blocksworld planning accuracy from 49.8% to 89.3% and setting new records across multiple planning benchmarks.
The article analyses recent breakthroughs such as OpenAI's o1, Long CoT, and test‑time search, arguing that enabling LLMs to perform self‑critique and reinforcement learning with long output sequences is essential for future AI performance, while warning against overly structured workflows.
