[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-prompt-chaining::en":3,"gloss-cluster-prompt-chaining::en":20,"gloss-next-prompt-chaining::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"prompt-chaining","prompt-eng","Prompt Chaining","Prompt chaining is an application-design pattern where a complex task is decomposed into a sequence of smaller, more focused LLM calls, with the output of each step serving as (part of) the input to the next, rather than attempting the entire task in a single monolithic prompt. This mirrors the software-engineering principle of breaking a large function into smaller, testable units: each step in a chain is simpler, easier to prompt-engineer well, easier to evaluate independently, and easier to debug when something goes wrong, because a failure can be isolated to a specific step rather than diagnosed inside one giant, opaque prompt. Prompt chaining is foundational to how most non-trivial AI products are actually built — a single \"do everything\" prompt tends to underperform a well-designed chain on complex, multi-stage tasks, because each individual prompt in a chain can be shorter, more focused, and validated before moving forward (a step can even include a validation gate that stops the chain or retries if an intermediate output fails a check). Common chain patterns include: sequential (step A's output feeds step B, feeds step C), branching (a classification step routes to one of several different downstream chains based on its result), and map-reduce style (the same prompt runs over many independent chunks in parallel, then a final prompt combines\u002Fsummarizes all the results). This pattern is also the conceptual foundation of most \"AI agent\" frameworks, which are essentially prompt chains with added branching logic, tool calls, and loops. The trade-off versus a single prompt is added latency (multiple sequential API round-trips) and cost (multiple calls instead of one), which teams manage by running independent steps in parallel where possible and reserving chains for tasks where single-prompt accuracy genuinely isn't good enough. Concrete worked example: an AI blog-post generator SaaS chains three prompts instead of one: Step 1 — \"Given this topic and target keyword, generate 5 possible article outlines with H2 headers\" (output: 5 outlines); Step 2 — a selection\u002Fscoring prompt picks the strongest outline based on SEO criteria (output: 1 chosen outline); Step 3 — for each H2 in the chosen outline, a separate prompt call generates that section's content, referencing the overall article context (output: full article body). Attempting this as one giant prompt (\"write a full SEO-optimized 2000-word article about X\") reliably produces generic, shallow content; the chained version produces a more deliberately structured, higher-quality result because each step has one clear job.","Prompt chaining breaks a complex task into a sequence of smaller prompts, where each step's output feeds the next step's input.",null,[11,14,17],{"slug":12,"name":13},"agent","Agent",{"slug":15,"name":16},"prompt-template","Prompt Template",{"slug":18,"name":19},"react-prompting","ReAct Prompting",[21,25,28,31,35,38,41,44,47,50,53,56],{"slug":22,"category":5,"name":23,"updated_at":24},"analogical-prompting","Analogical Prompting","2026-08-24T02:46:37+00:00",{"slug":26,"category":5,"name":27,"updated_at":24},"automatic-prompt-optimization","Automatic Prompt Optimization",{"slug":29,"category":5,"name":30,"updated_at":24},"chain-of-density","Chain of Density (CoD)",{"slug":32,"category":5,"name":33,"updated_at":34},"chain-of-thought-prompting","Chain-of-Thought Prompting","2026-08-24T02:46:36+00:00",{"slug":36,"category":5,"name":37,"updated_at":24},"chain-of-verification","Chain-of-Verification",{"slug":39,"category":5,"name":40,"updated_at":34},"chunking","Chunking",{"slug":42,"category":5,"name":43,"updated_at":34},"constrained-decoding","Constrained Decoding",{"slug":45,"category":5,"name":46,"updated_at":34},"context-stuffing","Context Stuffing",{"slug":48,"category":5,"name":49,"updated_at":34},"delimiter","Delimiter",{"slug":51,"category":5,"name":52,"updated_at":24},"directional-stimulus-prompting","Directional Stimulus Prompting",{"slug":54,"category":5,"name":55,"updated_at":24},"emotion-prompting","Emotion Prompting",{"slug":57,"category":5,"name":58,"updated_at":34},"few-shot-prompting","Few-Shot Prompting"]