AI Prompt Step

The AI Prompt step makes one AI call and returns the result to the steps that follow. It is the building block for classification, summarisation, extraction, translation, and anything else that fits in a single call.

It runs on Glow’s managed infrastructure. There is no API key to configure, no provider to connect and no model to choose: write the instructions and run it. Usage is billed through your Glow plan.

For tasks that need tools, multiple reasoning steps, or memory, use the AI Agent step instead. To pull several values out of one messy text without writing a prompt, AI Data Transform does it with one instruction per value.

Setting it up

The fields

Response shape sits in the main form, directly below Instructions. Parse JSON result sits under Optional Props. There is no model dropdown. See Models for how the AI steps differ on this.

Getting the same fields every time

A model left to itself will phrase its answer differently from run to run, which is fine for a summary and a problem for anything a later step reads. Response shape fixes that: you describe the structure you want, and the model is held to it while it writes rather than checked afterwards.

Turning it on opens the shape editor. In the Fields view, add one row per value you want back. Name each field, pick its kind, and mark it required or optional. The kinds are text, number, yes/no, list, and group, where a group holds nested fields of its own.

If the step has already run successfully, Glow reads the last answer and offers its fields as a starting point. Click Use this to accept them, or Set it up myself to start from a blank shape.

Already have a schema written down? The JSON tab takes a JSON Schema directly. There, a shape for a support ticket might read:

{
  "type": "object",
  "properties": {
    "category": { "type": "string" },
    "urgency": { "type": "string" },
    "needsHuman": { "type": "boolean" }
  },
  "required": ["category", "urgency"]
}

Downstream steps then read {{ 2.result.category }} and {{ 2.result.urgency }} knowing both are always there.

Turning Response shape off keeps the shape you built. Turn it back on and it is there again, so you can pause enforcement without rebuilding anything.

If the answer does not match the shape, the step fails and says which part was wrong. A malformed answer stops the run rather than travelling on as something a later step misreads.

Ask the AI to fix a wrong shape changes that. Turn it on and a mismatched answer is sent back once, with the problem described, and the corrected answer is used. It costs one extra AI call on the runs that need it, so the better first move is to describe the shape in your instructions as well. The retry is then a safety net rather than part of the normal path.

Referencing data

Include data from earlier steps by referencing their step number inside the instructions.

Classify the following support ticket into one of these categories:
billing, technical, account, other.

Ticket subject: {{ 1.subject }}
Ticket body: {{ 1.content }}

Respond with only the category name.

To pass a step’s entire output, reference the step number alone:

Summarize this email: {{ 3 }}

Full syntax is documented in Variable Reference Syntax.

What it passes on

result keeps the shape of what the AI returned. Ask for a sentence and it holds a sentence; ask for a list of objects and it holds a list of objects, ready for a later step to walk into or loop over.

Extract the following fields from the invoice text below. Return valid JSON
with keys: vendor_name, invoice_number, date, total_amount.

Invoice text:
{{ 1.invoice_text }}

A run asking for a joke comes back as:

{ "result": "Why don't skeletons fight each other? They don't have the guts." }

and one asking for three people as:

{
  "result": [
    { "name": "Ann", "city": "Prague" },
    { "name": "Bob", "city": "Brno" }
  ]
}

so {{ 2.result.vendor_name }} and {{ 2.result.0.name }} both work when the answer has that shape.

When the shape matters, set Response shape. The answer then always has the fields you named, and later steps can read them with no parsing step. Parse JSON result covers the other case: your instructions ask for JSON but you have not set a shape. It tells the model to answer in clean JSON with no markdown around it, and parses the text so later steps can select individual fields.

Limits

Missing input

If a referenced value is empty, that part of the prompt arrives blank and the model works with what remains. That usually produces a confident but wrong answer. Add a Condition step before the AI Prompt to check that required data is present.

The answer arrives in the wrong shape

Left to itself a model phrases things differently from one run to the next. Where the shape matters, set Response shape and the answer is held to it: see Getting the same fields every time.

High-volume runs

Every run of the workflow is a model call. Before scaling a workflow to thousands of daily runs, check the effect on both cost and execution time, and see System Limits for the ceilings that apply to your plan.

Examples to copy

You are a support ticket classifier. Read the ticket below and respond
with exactly one category: billing, technical, account, or other.

Subject: {{ 1.subject }}
Body: {{ 1.content }}

Summarize the following email in two sentences. Focus on action items
and key decisions.

From: {{ 2.from }}
Subject: {{ 2.subject }}
Body: {{ 2.body }}

Extract the following fields from the invoice text below. Return valid
JSON with keys: vendor_name, invoice_number, date, total_amount.

Invoice text:
{{ 1.invoice_text }}

What’s Next?

• Need tools, memory, or multi-step reasoning? Use the AI Agent step.
• Look up reference syntax in Variable Reference Syntax.