Prompt Chaining: 5 Recipes That Ship (2026)
Five paste-ready prompt chaining recipes for 2026: extract-then-reason, draft-critique-rewrite, route, map-reduce, and generate-then-gate, each with its failure mode.
Every prompt recipe. Copy, paste, ship.
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Five paste-ready prompt chaining recipes for 2026: extract-then-reason, draft-critique-rewrite, route, map-reduce, and generate-then-gate, each with its failure mode.
Five paste-ready meta prompting recipes: make the model write, critique, rubric, and template your prompts, each with its failure mode. Plus the honest part: when meta prompting just wastes tokens.
Four prompt-layer defenses against prompt injection that measurably help, three that are theater, and the one architecture rule that actually keeps you safe. With paste-ready prompts and each failure mode.
Five copy-paste few-shot prompting recipes for July 2026: lock output format, tone, labels, and edge cases. Each with the prompt and the exact case where it breaks.
Extended thinking changed in July 2026: on Claude Sonnet 5 and Opus 4.8 you use adaptive thinking and effort, not budget_tokens. Three recipes and the gotchas.
Three production prompt patterns for Anthropic's Claude Tag in Slack channels, plus two failure modes that bite in week one. June 2026.
Three production Claude tool use recipes tested on Sonnet 4.6, Opus 4.7, and Haiku 4.5 with current pricing. Plus the two failure modes nobody warns you about. June 2026.
Three Claude prompt-caching recipes with real cost math for Sonnet 4.6, Opus 4.7, and Haiku 4.5. Plus two patterns where caching quietly costs you 25% more than not using it.
Three production-grade Claude structured output recipes for June 2026. Invoice extraction on Sonnet 4.6, support triage on Haiku 4.5, NL to SQL on Opus 4.7. Real cost per call. Three failure modes the docs do not warn you about.
Wrap a good prompt in one route handler: validate input, call the model, validate output, return typed JSON. No agent framework, no vector DB, no queue. A prompt plus a POST endpoint is a shippable feature. Add the rest only when a real user complains. Ship the prompt, not the platform.
Step one: ask for JSON. Step two: if JSON.parse throws, send the broken string plus the parser error back and ask only for a corrected version. Don't re-explain the task. Models fix syntax far more reliably than they self-correct semantics. Two steps, near-zero unparseable output.
Before you ship a generated answer, run one cheap second call that checks it against the source. Ask: is every claim in the answer supported by the provided context? Get back yes/no plus the first unsupported claim. Reject on no. Five lines of glue, catches the bulk of confident fabrications.