Few-shot examples: the prompt tweak behind a claimed 5× jump in agentic coding

A Towards Data Science article published yesterday argues that few-shot prompting can deliver a roughly 5× boost in agentic coding output quality by anchoring the agent with concrete examples.

• The post frames “few-shot” as supplying references (like an existing repo, prior outputs, or screenshots) so an agent can copy patterns instead of inferring intent from a spec alone.
• It recommends keeping a working “pattern library” of reusable materials—folders, past prompt snippets, and repeatable task templates—so agents can start from known-good structure.
• One example uses Claude Code to reuse a GitHub Actions validation workflow from another repository, while changing a single step to match the current project.
• For non-code work, it describes iterating on presentation/marketing assets by recording change requests with MacWhisper, then feeding the transcript back to the agent for revisions.
• It also calls out slash-command style shortcuts as a way to package and reuse proven prompt recipes across tasks.