Advanced Prompt Chaining Guide

Advanced Best Practices for Prompt Chaining in SmartForms

SmartForms support prompt chaining for multi-step AI workflows. Each step builds on earlier results. This improves structure and consistency.

Example workflow:

• Generate a customer avatar.

• Draft a value proposition.

• Convert it into an email sequence.

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How prompt chaining works

When a SmartForm has Prompt Instructions, it runs one model call. That call uses mapped form fields and any backend data sources.

You can add more prompt steps. Each step runs as a separate API call. Each step produces its own output.

Later steps can:

• Reuse earlier outputs with @step_1, @step_2, and so on.

• Run a new task with different instructions and context.

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Reference earlier steps with @step_x

Use @step_1, @step_2, and so on inside later instructions. Treat each reference as raw text from the earlier output.

Example

Write a cold email introducing the new offer.
Use the customer avatar below.

Customer avatar:
@step_1

Best practices:

• Add a label before the reference.

• State what the referenced content represents.

• Specify the output format you want.

Avoid vague instructions like:

Use @step_1 to generate the copy.

Prefer explicit instructions like:

Using the brand voice below, write 5 Google ad headlines.

Brand voice:
@step_1

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Prompt chaining design patterns

Define clear stages

Split the workflow into small steps. Keep each step focused.

Common staging:

• Step 1: Generate core data (avatar, outline, research summary).

• Step 2: Generate the primary asset (copy, plan, script).

• Step 3: Refine and format (tone, structure, length).

• Step 4: Repurpose (emails, ads, bullets, slides).

Make each step self-contained

Do not assume the model remembers your intent. Restate the goal of the step. Explain what the referenced content contains.

Example:

Rewrite the value proposition in a more emotional tone.
Use the tone guide below.

Tone guide:
@step_1

Reuse SmartForm fields in later steps

You can mix @step_x outputs with mapped fields like @q1answer.

Write 3 Instagram captions for @q1answer.
Use the product description and brand voice below.

Product description:
@step_1

Brand voice:
@step_2

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Token, cost, and timeout considerations

Each prompt step is a separate API call. Each call has its own token budget. Large outputs early can reduce quality later. They also increase cost and latency.

Practical guidance:

• Use GPT-5.2 for deeper reasoning and better formatting.

• Keep step outputs short when they will be reused downstream.

• Avoid repeating large backend content in every step.

• Avoid stacking web browsing, scraping, and large multi-step chains.

Common symptoms of oversized chains:

• Truncated outputs.

• A step failing mid-run.

• The full SmartForm timing out.

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Data source considerations

Backend data sources can be expensive in tokens. Referencing them in many steps increases usage.

Recommended pattern:

• Inject the data source in step 1.

• Summarize or extract what you need.

• Pass the summary downstream via @step_1.

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Checklist

• Break the workflow into clear steps.

• Add context labels before @step_x references.

• Keep reusable step outputs concise.

• Pick a model that matches your token needs.

• Avoid re-ingesting large data sources across steps.

• Test each step before publishing the SmartForm.