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Send the existing transcript as text in a Chat Completions message, tell the model what to extract, and set response_format to JSON Schema when your chosen model supports it. A schema helps constrain the response’s shape; it does not prove that the summary is accurate or faithful to the transcript, so validate the result in your application.
What this workflow does—and does not do
This implementation starts with a sales-call transcript you already have. It sends that text to the Chat Completions API and requests a structured summary. It does not capture a recording or transcribe audio.
OpenAI documents Chat Completions as generating a response from a list of messages. Its create endpoint is POST https://api.openai.com/v1/chat/completions. The API reference describes three response-format choices: ordinary text, JSON Schema, and JSON object mode. See the Chat Completions API reference for the current parameters and model support.
Choose the JSON response format
Use the format that matches how much control your application needs. JSON Schema is the documented option for Structured Outputs on supported models. JSON object mode is the older JSON mode: it is intended to ensure valid JSON, but does not provide the same schema-conformance guarantee. Ordinary text is appropriate when your application does not need a machine-readable object.
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| Format | What to expect | Implementation decision |
|---|---|---|
| Ordinary text | A text response, without a JSON-format requirement. | Use when a person will read the summary or your application does not depend on fixed fields. |
| JSON object | Valid JSON, without the same guarantee of conformance to a defined schema. | Use only if JSON syntax is sufficient and your application will validate the expected fields itself. |
| JSON Schema | Structured output constrained by a supplied schema when the selected model supports it. | Prefer this for a defined response shape, after confirming support for the model and parameters you plan to use. |
Parameter support can differ by model. Check the API reference for the model you select rather than assuming every model accepts every response format. A structurally valid response can still omit a useful interpretation or misstate what the caller said.
Define an application-owned summary schema
OpenAI’s reference documents the response-format mechanism, not a sales-call schema. The following fields are an illustrative design choice, not an official or proven set of best fields. Adapt them to your workflow and decide how your application should represent unknown information.
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{
"type": "object",
"properties": {
"call_summary": { "type": "string" },
"customer_needs": { "type": "array", "items": { "type": "string" } },
"objections": { "type": "array", "items": { "type": "string" } },
"commitments": { "type": "array", "items": { "type": "string" } },
"follow_up_actions": { "type": "array", "items": { "type": "string" } }
},
"required": [
"call_summary",
"customer_needs",
"objections",
"commitments",
"follow_up_actions"
],
"additionalProperties": false
}This example expresses a possible object shape; confirm the exact schema syntax and supported constraints in the current API reference before using it. In your instructions, tell the model to use only information supported by the transcript and to represent absent information consistently, such as with an empty array. That instruction is a prompt-design choice, not a guarantee of factual accuracy.
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Put the task instructions and transcript in the message list sent to the create endpoint. The prompt below is an illustrative starting point, not a sales-specific prompt prescribed by OpenAI. Add the response-format configuration for the schema you defined, following the endpoint reference and the syntax supported by your selected model.
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POST https://api.openai.com/v1/chat/completions
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json
{
"model": "YOUR_SUPPORTED_MODEL",
"messages": [
{
"role": "system",
"content": "Summarize the sales-call transcript using only information stated in it. Do not infer unstated facts. Use empty arrays when a requested list has no supported items."
},
{
"role": "user",
"content": "Extract a concise summary, customer needs, objections, commitments, and follow-up actions from this transcript:nnTRANSCRIPT TEXT"
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "sales_call_summary",
"strict": true,
"schema": "INSERT THE SCHEMA OBJECT HERE"
}
}
}The request shows the endpoint and message-based pattern, but the schema placeholder is explanatory, not a literal value to send. Replace it with the schema object and use the exact response-format structure supported by your chosen model. Keep API keys on the server or in another appropriately protected environment; do not expose a secret key in browser code.
For initial setup, OpenAI’s Developer quickstart covers creating an API key, installing an SDK, sending a request, and reading a response. Its current example uses the Responses API rather than Chat Completions.
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Validate the response before using it
Structured output addresses shape, not meaning. Before writing a summary into a CRM, creating tasks, or presenting it as a reliable record, add application-level checks suited to the consequences of an error.
- Check that the response completed and contains the expected output; do not treat a missing or incomplete generation as a usable summary.
- Parse and validate the returned object against your application’s expected fields, including required fields and value types.
- Check whether each claimed need, objection, commitment, and action is actually supported by the transcript. Where a decision has operational or customer consequences, consider requiring human review.
- Define how to handle missing, ambiguous, or conflicting transcript details instead of silently converting them into asserted facts.
These are prudent implementation checks, not measured performance guarantees. The cited API documentation does not establish sales-domain accuracy, a best-performing summary schema, or an evaluation benchmark.
Should a new integration use Chat Completions?
The Chat Completions API reference says: “Starting a new project? We recommend trying Responses to take advantage of the latest OpenAI platform features.” OpenAI also links a comparison of Chat Completions and Responses. If you are beginning a new integration, compare the current API documentation against your feature needs before choosing an endpoint. Chat Completions remains a documented message-based implementation path, but the recommendation for new projects is to consider Responses.
