> ## Documentation Index > Fetch the complete documentation index at: https://docs.corti.ai/llms.txt > Use this file to discover all available pages before exploring further. # Create a Section > Author, version and publish reusable sections to compose into templates and to use in guided document generation Beta The `/documents/sections` endpoints — including section versions and publish — are available in beta. Feedback welcome at [help@corti.ai](mailto:help@corti.ai) or via your Corti contact. ## What is a section? A **section** is a reusable, versioned building block for document generation. It defines a single piece of structured output — for example a *Clinical Findings* section or an *Allergies* section — with its own: * **`heading`** — a heading passed to the LLM and used in rendered output. * **`instructions`** — the prompts that drive content selection, writing style and any miscellaneous guidance. * **`outputSchema`** — the shape of the output (string, number, boolean, array or object). The schema is also a prompting surface: descriptions, enums, patterns, formats and min/max constraints all flow into how the LLM generates the content. Sections are first-class resources. You can list, version, publish and reuse them across many templates. ## When to author your own sections | Scenario | Why a custom section | | :----------------------------------------------------------------------------------------------------- | :------------------------------------------------------------ | | You want a section that doesn't exist in the Corti standard library (e.g., a specialty-specific field) | Author once, reuse everywhere | | You want stable, versioned control over the prompt for a section your customers rely on | Version & publish lifecycle | | You want a structured output (object/array with fields) instead of free-text | `outputSchema` lets you constrain shape | | You want to fine-tune a Corti default section while still inheriting future improvements | Use `inheritFromId` — see [Inheritance](/textgen/inheritance) | ## Section lifecycle at a glance `POST /documents/sections` creates the section resource and its **version 0**. By default the new version is automatically published. Set `publish: false` to prevent the new section being returned in `LIST /documents/sections` `POST /documents/sections/{sectionID}/versions` creates a new version with a fresh `generation` config. New versions are *not* automatically published — the previously published version stays live until you explicitly publish a new one. `POST /documents/sections/{sectionID}/versions/{versionID}/publish` sets that version as the published version. Only the published version is referenced by templates and used at generation time. Reference the section by `id` from a template, or compose sections directly in a `POST /documents` request. See [Create a Template](/textgen/template-creation). A section must be **published** before it can be referenced by a published template version or used in generation. `publish: true` is the default on creation. ## Create your first section ### Minimal request — string output The simplest section produces a free-text string. You only need `name` and a `generation` block with `heading`, `instructions.contentPrompt` and an `outputSchema` of `type: string`. Optionally add `languages`, `regions`, `specialties` and `labels` if you want the section to be discoverable by those filters. ```ts title="JavaScript" expandable theme={null} // Replace these with your values const ACCESS_TOKEN = ""; const section = await client.documents.sections.create({ name: "Clinical Findings", languages: ["en"], description: "Summarises the key clinical findings from the interaction.", labels: [{ key: "category", value: "clinical" }], generation: { heading: "Clinical Findings", instructions: { contentPrompt: "Extract all relevant clinical findings mentioned during the interaction.", writingStylePrompt: "Use concise, professional medical language.", }, outputSchema: { type: "string" }, }, }); ``` ```csharp title="C# .NET" expandable theme={null} using Corti; var section = await client.Documents.Sections.CreateAsync( new GuidedSectionsCreateFromScratchRequest { Name = "Clinical Findings", Languages = new[] { "en" }, Description = "Summarises the key clinical findings from the interaction.", Labels = new[] { new GuidedLabel { Key = "category", Value = "clinical" } }, Generation = new GuidedSectionGeneration { Heading = "Clinical Findings", Instructions = new GuidedSectionInstructions { ContentPrompt = "Extract all relevant clinical findings mentioned during the interaction.", WritingStylePrompt = "Use concise, professional medical language.", }, OutputSchema = new GuidedStringNode(), }, }); ``` ```python title="Python" expandable theme={null} import requests # Replace these with your values ENVIRONMENT = "" TENANT = "" TOKEN = "" response = requests.post( f"https://api.{ENVIRONMENT}.corti.app/v2/documents/sections", headers={ "Authorization": f"Bearer {TOKEN}", "Tenant-Name": TENANT, "Content-Type": "application/json", }, json={ "name": "Clinical Findings", "languages": ["en"], "description": "Summarises the key clinical findings from the interaction.", "labels": [{"key": "category", "value": "clinical"}], "generation": { "heading": "Clinical Findings", "instructions": { "contentPrompt": "Extract all relevant clinical findings mentioned during the interaction.", "writingStylePrompt": "Use concise, professional medical language.", }, "outputSchema": {"type": "string"}, }, }, ) response.raise_for_status() section = response.json() ``` ```bash title="cURL" expandable theme={null} # Replace these with your values ENVIRONMENT="" TENANT="" TOKEN="" curl --request POST \ --url "https://api.${ENVIRONMENT}.corti.app/v2/documents/sections" \ --header "Authorization: Bearer ${TOKEN}" \ --header "Tenant-Name: ${TENANT}" \ --header "Content-Type: application/json" \ --data '{ "name": "Clinical Findings", "languages": ["en"], "description": "Summarises the key clinical findings from the interaction.", "labels": [{ "key": "category", "value": "clinical" }], "generation": { "heading": "Clinical Findings", "instructions": { "contentPrompt": "Extract all relevant clinical findings mentioned during the interaction.", "writingStylePrompt": "Use concise, professional medical language." }, "outputSchema": { "type": "string" } } }' ``` The response is the created section, including its auto-published version 0: ```json title="Section response" expandable theme={null} { "id": "", "source": "user", "name": "Clinical Findings", "languages": ["en"], "regions": [], "specialties": [], "description": "Summarises the key clinical findings from the interaction.", "labels": [{ "key": "category", "value": "clinical" }], "publishedVersion": { "id": "", "versionNumber": 0, "generation": { "heading": "Clinical Findings", "instructions": { "contentPrompt": "Extract all relevant clinical findings mentioned during the interaction.", "writingStylePrompt": "Use concise, professional medical language." }, "outputSchema": { "type": "string" } } }, "createdAt": "2026-05-12T10:00:00Z", "updatedAt": "2026-05-12T10:00:00Z" } ``` Keep the `name` and `description` human-friendly — they are *not* passed to the LLM. The `heading` and `instructions` are what the model sees. ## Configuring the `generation` block ### `instructions` | Field | Purpose | | :--------------------------- | :--------------------------------------------------------------------------------------------------------------------------------- | | `contentPrompt` *(required)* | What the section should include. In `documentationMode: routed_parallel`, this also drives which facts get routed to this section. | | `writingStylePrompt` | Tone, register and style (e.g. telegraphic, lay-person, formal narrative). | | `miscPrompt` | Free-form prompt for anything that doesn't fit content or style — e.g. formatting nuances, fixed disclaimers, edge-case handling. | ### `outputSchema` `outputSchema` declares the *shape* and also acts as an additional prompting surface. The discriminator is `type`: * **`string`** — free text, optionally constrained by `default`, `enum`, or `pattern`. * **`number`** — integer or float, optionally constrained by `enum`, `minimum`, `maximum`, `default`. * **`boolean`** — `true`/`false`, with an optional `default`. * **`array`** — list of items of any schema type; configure `itemFormat` (a format string that must contain the `{item}` placeholder, e.g. `"- {item}\n"`), and `minItems`/`maxItems`. * **`object`** — multi-field structured output. Declare `fields[]` (each with its own `key`, `description`, `value` schema). Control rendering with `fieldFormat` — either as a per-field iteration template using generic `{key}` / `{value}` placeholders (e.g. `"{key}: {value}\n"` inline, `"{key}\n{value}\n"` block) or as a fully custom layout referencing specific field keys (e.g. `"{test}: {result}"`). The `description` on a schema node is a prompting field too — use it to give the model targeted guidance that supplements `instructions`. The summary above is just the shape. For required-vs-optional fields per node type, the regex/enum/default behavior on `string`, the `itemFormat` and `fieldFormat` rendering primitives (both per-field iteration and fully custom layouts), and copy-paste clinical examples (fixed subheadings with explicit defaults, dynamic organ-system labels, mixed-type test results, EHR placeholders, prefixes like `Rp.`), see the **[Section Schemas guide](/textgen/section-schemas)**. ### Example — structured object output A *Vital Signs* section that returns a structured object with consistent rendering: ```json title="Section with object outputSchema" expandable theme={null} { "name": "Vital Signs", "languages": ["en"], "labels": [ { "key": "category", "value": "clinical" }, { "key": "category", "value": "exam" } ], "generation": { "heading": "Vitals", "instructions": { "contentPrompt": "Extract vitals discussed during the encounter: blood pressure, heart rate, respiratory rate, temperature and SpO2. Omit any that were not mentioned.", "writingStylePrompt": "Telegraphic — values only, no narrative." }, "outputSchema": { "type": "object", "fieldFormat": "**{key}**: {value}", "fields": [ { "key": "Blood pressure", "description": "Systolic/diastolic in mmHg.", "value": { "type": "string", "pattern": "^\\d{2,3}/\\d{2,3}$" } }, { "key": "Heart rate", "description": "Beats per minute.", "value": { "type": "number", "minimum": 0, "maximum": 300 } }, { "key": "Respiratory rate", "description": "Breaths per minute.", "value": { "type": "number", "minimum": 0, "maximum": 80 } }, { "key": "Temperature", "description": "In °C.", "value": { "type": "number" } }, { "key": "SpO2", "description": "Percent oxygen saturation.", "value": { "type": "number", "minimum": 0, "maximum": 100 } } ] } } } ``` ### Example — array of items A *Diagnoses* section returning a numbered list, capped at five items: ```json title="Section with array outputSchema" expandable theme={null} { "name": "Diagnoses", "languages": ["en"], "generation": { "heading": "Diagnoses", "instructions": { "contentPrompt": "List the diagnoses discussed or established during the encounter, most relevant first." }, "outputSchema": { "type": "array", "itemFormat": "* {item}\n", "minItems": 0, "maxItems": 5, "items": { "type": "string" } } } } ``` ## Next steps Create inheriting variants of this section to scale across customers, departments and specialties. Iterate by creating new versions and publishing them on your own cadence. Read resolved-vs-raw, update metadata, delete with inheritance guards, filter LIST results. Compose your sections into a versioned template that can be referenced at generation time.