Question

Best practices for handling nested JSON payloads across multi-step LLM Zaps

16 days ago
October 7, 2026
4 replies
55 views

animesalt
Beginner

Hi everyone,

I've been experimenting with multi-step workflows that pass structured data generated via custom AI or LLM actions into downstream database and task management steps.

While simple string parameters map over cleanly, I've run into occasional formatting snags when trying to pass complex, nested JSON arrays or multi-line text blocks—specifically where downstream apps expect flat fields or strict schemas, causing the payload mapping to occasionally break or truncate data fields.

For those building advanced workflows involving automated data transformation or custom payload parsing, what intermediate formatting steps or structural patterns do you rely on to ensure data consistency before it hits the final action step?

Would love to hear how others handle payload sanitization in multi-step setups!

+14

Troy Tessalone
Zapier Orchestrator & Solution Partner
16 days ago
October 7, 2026

Hi @animesalt

Try this Zap action: AI by Zapier

You can output designated data points with specific formats and handle arrays.

Then you can use those to create your JSON, possibly via a Code step.

Help: https://help.zapier.com/hc/en-us/articles/8496342944013-Use-AI-by-Zapier-to-analyze-and-return-data

⚡ Troy Tessalone - AutomationAce.com | Top Zapier Solution Partner | #1 Zapier Community Contributor

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B

base64to.xyz
New
15 days ago
October 7, 2026

Hello

Have you looked into https://zapier.com/apps/json-helper/integrations

Put all of your JSON through this and it’ll organize and flatten it for you. No code required.

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D

DHolloway
New
9 days ago
October 14, 2026

Hi everyone,

I've been experimenting with multi-step workflows that pass structured data generated via custom AI or LLM actions into downstream database and task management steps.

While simple string parameters map over cleanly, I've run into occasional formatting snags when trying to pass complex, nested JSON arrays or multi-line text blocks—specifically where downstream apps expect flat fields or strict schemas, causing the payload mapping to occasionally break or truncate data fields.

For those building advanced workflows involving automated data transformation or custom payload parsing, what intermediate formatting steps or structural patterns do you rely on to ensure data consistency before it hits the final action step?

Would love to hear how others handle payload sanitization in multi-step setups!

Troy's suggestion to use AI by Zapier for reshaping outputs is the simplest starting point, since it can flatten nested structures without code. For anything more stubborn, a Code by Zapier step offers full control over parsing and sanitising before the payload moves downstream. The usual failure point is apps expecting flat fields while the LLM returns nested arrays. Testing with a single record first saves a good deal of guessing later.