Overview
How might we design prompt chaining so people can trust and act on AI output?
When this pattern fits
- Ideal for workflow automation tools, AI agent platforms, and complex reasoning applications where transparency about the agent's thought process builds user trust.
When to skip or lighten it
- Single-turn Q&A where a chain adds latency with no quality gain.
- Products that cannot show or edit intermediate outputs.
- Hard real-time voice turns where multi-step pipelines miss the conversational window.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Invisible chains that surprise users with delayed or multi-call cost.
No way to stop, skip, or edit a bad intermediate step.
Chains that re-run from scratch after a tiny mid-step edit.
Treating orchestration infra (LangChain graphs) as the user-facing model without a product metaphor.
How products use it
| Product | Implementation |
|---|---|
| LangChain / LangGraph apps | Developer-defined step graphs; products surface steps as stages or nodes. |
| CrewAI-style agents | Role-based sequential tasks with handoffs between specialist agents. |
| ChatGPT custom GPTs / Actions | Multi-tool sequences users experience as research → answer stages. |
| Cursor agent plans | Plan steps then execute code edits as a visible multi-step run. |
Implementation
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Prompt Chaining" AI interface design pattern.
Pattern Definition:Frequently asked questions
What is prompt chaining in AI UX?
Prompt chaining splits work into ordered model steps where each step’s output becomes input for the next, with UI that shows progress and preferably lets users edit intermediates.
How is chaining different from agent orchestration?
Chaining is usually a linear or lightly branched sequence of prompts. Orchestration coordinates multiple agents, tools, and control flow, often visualized as a graph, not only a list.
When should designers show each chain step?
Show steps when latency, cost, or failure modes matter, or when users need to correct mid-pipeline output. Hide only trivial internal helpers that never affect the user-visible result.
Does chaining reduce hallucinations?
It can, when steps separate retrieval, drafting, and verification. It does not help if every step is another unconstrained generation with no grounding or checks.