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To summarize an existing sales-call transcript with Chat Completions, send the transcript and instructions as conversation messages to POST https://api.openai.com/v1/chat/completions, then request either JSON Schema structured output or JSON object mode. Use JSON Schema when your chosen model supports it and your application needs defined fields; validate the returned content before relying on it.
What this workflow does—and does not do
This implementation starts with transcript text you already have. It covers sending that text to the API and asking for a structured summary; it does not cover recording a call or transcribing audio.
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OpenAI describes Chat Completions as generating a response from a list of messages. The create endpoint is POST https://api.openai.com/v1/chat/completions. The API reference documents response formats, but does not prescribe a sales-call prompt or summary schema, nor does it establish sales-specific accuracy results.
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Choose a response format
| Format | What it is for | What to check |
|---|---|---|
| JSON Schema | Structured Outputs for a defined shape, when supported by the selected model. | Confirm that the model supports the required response-format parameters. Schema conformance does not establish that the summary’s claims are true. |
| JSON object | The older JSON mode; it is intended to ensure the response is valid JSON. | It does not provide the same schema-conformance guarantee as JSON Schema. Your application still needs to check the returned fields and values. |
| Text | Ordinary text output when a machine-readable object is not required. | Do not assume the response will follow a JSON structure unless you request and validate one. |
OpenAI recommends JSON Schema for models that support it. Parameter support can vary by model, so check the current API reference for the model and format you plan to use.
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Send transcript text with summarization instructions
Put instructions and the transcript in the message list. The following is an illustrative JavaScript pattern, not an OpenAI-prescribed sales prompt or tested example. Replace the model name with one whose current documentation supports the selected response format, and adapt the schema to your application.
const response = await fetch("https://api.openai.com/v1/chat/completions", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.OPENAI_API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "YOUR_SUPPORTED_MODEL",
messages: [
{
role: "system",
content: "Summarize only information supported by the transcript. If a field is not stated, return an empty string or empty list as appropriate."
},
{
role: "user",
content: `Summarize this sales call using the requested fields.nnTranscript:n${transcript}`
}
],
response_format: {
type: "json_schema",
json_schema: {
name: "sales_call_summary",
strict: true,
schema: {
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
}
}
}
})
});
const result = await response.json();The example fields—summary, needs, objections, commitments and follow-up actions—are an application-owned design choice. Define what counts as a commitment or action for your use case, and decide how to represent information that the transcript does not contain. Check the current API reference for exact parameter support and request details before deployment.
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Validate the response before using it
A well-shaped object is not necessarily a faithful summary. Add application-level checks before saving it to a CRM, triggering tasks or showing it as established fact.
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- Check that the request completed and the response contains the expected message content.
- Parse the content as JSON and verify required fields and value types, even when using JSON Schema.
- Handle missing, empty or incomplete output explicitly; do not silently treat an unfinished generation as a complete summary.
- For important claims, compare extracted needs, objections, commitments and actions with the transcript. Keep a path for human review when the summary will drive consequential follow-up.
- Test with representative transcripts from your own workflow. The official pages cited here provide no sales-domain accuracy benchmark or evaluation result.
Should a new integration use Chat Completions?
Chat Completions is a documented option for message-based generation, but it is not OpenAI’s only API path. OpenAI’s Chat Completions API reference says: “Starting a new project? We recommend trying Responses to take advantage of the latest OpenAI platform features.” The official quickstart’s current example also uses Responses. If you are starting from scratch, compare the APIs against your feature needs and consult their current documentation rather than assuming their endpoints or behavior are interchangeable.
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For onboarding, OpenAI’s Developer quickstart covers creating an API key, installing an SDK, making a request and reading a generated response. For Chat Completions endpoint details, response-format options and model-specific support, consult the Chat Completions API reference and the linked Chat Completions and Responses comparison.
Quick Recap
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