Openai Audio

OpenAI audio

Use this skill when a user wants OpenAI audio production or understanding in a bounded request: generated narration, spoken product demos, podcast or interview transcription, subtitle timing, translation to English, audio QA, or adding audio input/output to an existing chat workflow.

Do not use this as the primary guide for continuous low-latency speech-to-speech agents, live interpreting, SIP/WebRTC sessions, or voice activity detection. Those belong to the separate realtime voice skill. This skill may still explain the boundary: request-based audio APIs fit files, scripts, and bounded requests; realtime sessions fit open connections with live audio events. Documented source: OpenAI’s audio guide distinguishes request-based APIs, realtime sessions, and multimodal chat completions; verified 2026-07-10: https://developers.openai.com/api/docs/guides/audio

Source status and evidence labels

Treat the following as documented facts verified on 2026-07-10 from OpenAI public docs:

• The request-based speech endpoint is POST /v1/audio/speech; it accepts gpt-4o-mini-tts, the current pinned snapshot gpt-4o-mini-tts-2025-12-15, tts-1, and tts-1-hd, and supports built-in voices plus custom voice IDs where available. Speech input is limited to 4,096 characters. The older pinned snapshot gpt-4o-mini-tts-2025-03-20 is scheduled to shut down on 2026-07-23 with gpt-4o-mini-tts-2025-12-15 as its documented substitute; do not confuse that snapshot retirement with retirement of the unpinned alias. Sources: https://developers.openai.com/api/reference/resources/audio/subresources/speech/methods/create and https://developers.openai.com/api/docs/deprecations
• gpt-4o-mini-tts supports instruction-style voice control; the older tts-1 and tts-1-hd models do not support the instructions parameter. Source: https://developers.openai.com/api/reference/resources/audio/subresources/speech/methods/create
• Speech output formats for /v1/audio/speech are documented as mp3, opus, aac, flac, wav, and pcm; speed ranges from 0.25 to 4.0; streaming format can be sse or audio, with SSE not supported for tts-1/tts-1-hd. Source: https://developers.openai.com/api/reference/resources/audio/subresources/speech/methods/create
• The speech-to-text endpoint is POST /v1/audio/transcriptions; documented models include gpt-4o-transcribe, gpt-4o-mini-transcribe, gpt-4o-transcribe-diarize, and whisper-1, with response-format and streaming differences by model. File uploads are limited to 25 MB; split or compress larger inputs without cutting mid-sentence. Source: https://developers.openai.com/api/docs/guides/speech-to-text
• gpt-4o-transcribe-diarize does not support prompt, logprobs, or timestamp_granularities[]; input longer than 30 seconds requires chunking_strategy, with "auto" recommended. Source: https://developers.openai.com/api/docs/guides/speech-to-text
• Transcription streaming is supported for current GPT transcription models, but not for whisper-1; streamed transcription emits transcript delta/done events. Source: https://developers.openai.com/api/docs/guides/speech-to-text
• The translation endpoint is POST /v1/audio/translations; the API reference states that only whisper-1 is currently available for translation to English, even though shared model type enums may list additional audio model IDs. Source: https://developers.openai.com/api/reference/resources/audio/subresources/translations/methods/create
• Audio-capable chat completions can use gpt-audio-1.5 with modalities: ["text", "audio"], an audio object for voice/format, and input_audio content blocks for audio input. Source: https://developers.openai.com/api/docs/guides/audio
• Realtime routing is separate: the realtime overview points low-latency voice agents to gpt-realtime-2.1, live translation to gpt-realtime-translate, and live transcription to gpt-realtime-whisper. Source: https://developers.openai.com/api/docs/guides/realtime
• Pricing is volatile. As of 2026-07-10, OpenAI’s pricing page lists realtime/audio-generation prices by token or minute and transcription prices for gpt-4o-transcribe and gpt-4o-mini-transcribe by token with estimated per-minute costs. Always re-check before quoting costs in production. Source: https://developers.openai.com/api/docs/pricing
• OpenAI’s platform data-controls page says API endpoint data is not used for training, lists abuse-monitoring and application-state retention by endpoint, and says /v1/audio/transcriptions and /v1/audio/translations have no abuse-monitoring retention while /v1/audio/speech has 30-day abuse-monitoring retention by default; verified 2026-07-10. Source: https://developers.openai.com/api/docs/guides/your-data
• OpenAI’s usage policies prohibit use of someone’s likeness, including voice, without consent in ways that could confuse authenticity. The TTS guide also requires clear disclosure to end users that the voice is AI-generated, and custom-voice creation is limited to eligible customers and requires separate matching consent and sample recordings. Sources: https://openai.com/policies/usage-policies/ and https://developers.openai.com/api/docs/guides/text-to-speech

Treat the following as production heuristics, not OpenAI guarantees:

• Use gpt-4o-mini-tts first when the creative brief needs performance direction, expressive control, or iteration through natural-language voice notes.
• Use tts-1 only when low latency matters more than quality or detailed direction; use tts-1-hd only when an older integration already depends on it or when tests prove it wins for the specific voice/script.
• Use gpt-4o-transcribe when accuracy, proper nouns, or speaker-ready output is more important than cost; use gpt-4o-mini-transcribe for volume, rough cuts, drafts, and cost-sensitive batch transcription; use gpt-4o-transcribe-diarize when speaker labels matter.
• Use whisper-1 when you need translation-to-English or word/segment timestamp formats that are documented for whisper-1; otherwise prefer the newer transcription models after testing on representative audio.
• For finished media, generate high-quality WAV or FLAC intermediates, archive them, and encode MP3/AAC only for delivery copies.

Route the job before making an API call

Ask or infer these variables before choosing an endpoint:

1. Input: text script, audio file, live microphone stream, or a mixed chat turn with audio.
2. Output: audio file, transcript, translated English transcript, captions, diarized transcript, chat response with audio, or live audio events.
3. Time shape: bounded request versus continuous session.
4. Approval risk: celebrity/employee/customer voice, regulated transcript, public-facing ad, legal/medical/financial content, minors, or sensitive personal data.
5. Artifact custody: where raw inputs, generated audio, transcripts, sidecars, and approvals must be stored.

Decision table:

Never silently substitute a route. If the approved path is blocked, say what failed, whether it is model access, parameter incompatibility, policy, privacy, cost, or product mismatch, and ask before switching endpoint or model.

Text-to-speech production

Script preparation

Write for spoken performance before prompting. A TTS model can render pacing and tone, but it cannot rescue a script that is visually written, over-punctuated, or full of unexpanded abbreviations.

Production heuristics:

• Convert dense prose into breath-length lines. One idea per sentence is usually safer than nested clauses.
• Spell out pronunciation landmines in the script or instructions: product names, acronyms, people, non-English terms, numerals, units, URLs, and legal disclaimers.
• Use plain punctuation for prosody. Avoid theatrical markup unless the endpoint or downstream tool explicitly supports it.
• Keep pronunciation notes separate from creative performance notes when possible.
• For long narration, chunk at paragraph or scene boundaries and keep voice/instruction settings consistent across chunks.
• Leave a short room-tone or silence handle in post rather than trying to force exact silence inside the script.

Example performance brief shape:

Voice: coral
Model: gpt-4o-mini-tts
Format: wav for editing; mp3 only for review copies
Performance instructions:
Warm, confident startup launch voice. Conversational, not announcer-like.
Medium pace, lightly excited on the first sentence, then grounded.
Pronounce "Aster Lane" as "ASS-ter lane"; pronounce "SOC 2" as "sock two".
Do not impersonate any real person or named celebrity.

Documented controls:

• Built-in voices listed in the speech API reference include alloy, ash, ballad, coral, echo, fable, onyx, nova, sage, shimmer, verse, marin, and cedar; the TTS guide notes that voice availability depends on model, and older tts-1/tts-1-hd support a smaller set. Sources: https://developers.openai.com/api/reference/resources/audio/subresources/speech/methods/create and https://developers.openai.com/api/docs/guides/text-to-speech
• The TTS guide says voices are currently optimized for English and recommends marin or cedar for best quality; verified 2026-07-10. Source: https://developers.openai.com/api/docs/guides/text-to-speech
• The instructions field has a documented maximum length of 4096 characters and does not work with tts-1 or tts-1-hd. Source: https://developers.openai.com/api/reference/resources/audio/subresources/speech/methods/create

Voice selection

Do not describe voices only by demographic labels. Describe the performance role the production needs:

• “credible founder reading a release note”
• “gentle tutorial host”
• “premium documentary narrator”
• “fast but legible support IVR”
• “calm safety instruction”

Select 2 or 3 candidate voices, generate short auditions from the same representative line, and get approval before full-run generation. Use the exact final script or a dense excerpt, not a generic demo sentence, because pronunciation and pacing failures often appear only in the real copy.

Approval gate for public-facing voice:

1. Confirm the user has rights to the script and any referenced brand/character.
2. Confirm the selected built-in or custom voice is permitted for the intended use.
3. Confirm no request asks for confusing impersonation of a real person without consent.
4. Generate an audition no longer than needed to judge tone.
5. Store the selected voice, model, instructions, script version hash or filename, approver, date, and approved audition path.
6. Only then generate the full batch.

For a custom voice, also confirm organization eligibility, retain the provider consent-recording ID, verify that the actor in the consent and sample recordings is the same person, and follow the Text-to-Speech Supplemental Agreement. A general email approval is not a substitute for OpenAI's required consent-recording workflow.

Output formats and custody

Use format by lifecycle:

• wav or flac: editing masters, archival custody, downstream mixing.
• mp3 or aac: web preview, social draft, compact delivery.
• opus: speech-heavy streaming or low-bitrate distribution when supported by the target.
• pcm: low-level audio pipelines that require raw PCM.

Record every generated audio artifact with:

• source script path or exact script text;
• model;
• voice;
• instructions;
• response format;
• speed;
• generation date;
• OpenAI request ID if available in SDK logs;
• approval status;
• checksum for the output file;
• any post-processing steps.

Do not overwrite approved masters. If you regenerate a line, create a new versioned file and update the manifest.

Complete example: narrated product launch line

User intent: create one public-facing launch voiceover line, with approval before batch generation.

Model and endpoint: /v1/audio/speech, gpt-4o-mini-tts, voice marin, WAV master.

This call is the audition/sample, not authorization for a full batch. Run it only after the rights, disclosure, storage, and sample-cost preflight above; require recorded approval of this take before expanding the run.

Python example:

from pathlib import Path
from openai import OpenAI

client = OpenAI()

script = (
    "Aster Lane turns every customer call into a searchable product signal, "
    "so your team can fix what matters first."
)

instructions = (
    "Speak as a warm, credible product-launch narrator. "
    "Confident but not hypey. Medium pace. "
    "Pronounce Aster as ASS-ter. "
    "Do not imitate any real person."
)

out = Path("artifacts/audio/aster-lane-launch-v001.wav")
out.parent.mkdir(parents=True, exist_ok=True)

with client.audio.speech.with_streaming_response.create(
    model="gpt-4o-mini-tts",
    voice="marin",
    input=script,
    instructions=instructions,
    response_format="wav",
    speed=1.0,
) as response:
    response.stream_to_file(out)

Why it is structured this way:

• WAV is used because this is a production master.
• Pronunciation is specified explicitly.
• The performance direction is concise and avoids celebrity imitation.
• The file path encodes versioning rather than overwriting a prior take.

Review criteria:

• Correct pronunciation of “Aster”.
• Natural emphasis on “searchable product signal”.
• No ad-read exaggeration.
• No clipped start/end.
• Delivery has a clear AI-generated-voice disclosure plan.
• Loudness and silence handled in post, not assumed from raw model output.

Speech-to-text production

Choose the transcription model by downstream use

Documented facts:

• /v1/audio/transcriptions accepts audio file objects in formats listed by the API reference, including common compressed and uncompressed audio formats. Source: https://developers.openai.com/api/reference/resources/audio/subresources/transcriptions/methods/create
• The typed API reference says gpt-4o-transcribe and gpt-4o-mini-transcribe support only json, while the current speech-to-text guide says json or plain text and includes text examples. Treat this first-party mismatch as volatile: use json for portable production code unless a current account test confirms text. gpt-4o-transcribe-diarize supports json, text, and diarized_json, with diarized_json required for speaker annotations. Sources: https://developers.openai.com/api/reference/resources/audio/subresources/transcriptions/methods/create and https://developers.openai.com/api/docs/guides/speech-to-text
• whisper-1 supports timestamp granularities with response_format="verbose_json"; OpenAI’s guide says timestamp_granularities[] is only supported for whisper-1. Sources: https://developers.openai.com/api/docs/guides/speech-to-text and https://developers.openai.com/api/reference/resources/audio/subresources/transcriptions/methods/create

Production heuristics:

• For edit logs, legal-ish review, or named-entity-heavy interviews, prefer the higher-accuracy model and add a vocabulary prompt.
• For rough-cut triage across many hours, use the cheaper model, then re-transcribe selected segments with the more accurate model if needed.
• For captions, decide first whether you need word timing, segment timing, speaker labels, or only plain text. The required output shape may determine the model.
• For low-confidence or noisy segments, extract a clean WAV segment and retry with a vocabulary prompt rather than repeatedly sending the entire long file. Because the diarization model does not accept prompt, use a prompt-capable transcription model for the isolated repair and reconcile its text with the diarized timeline manually.

Transcription prompts

Use prompt to guide style, spelling, jargon, or continuation from previous segments. The prompt should match the audio language for transcription. Do not use the prompt to invent content or “fix” unclear audio; mark inaudible spans in post-review when necessary.

Good prompt contents:

• names and product terms;
• speaker names if known;
• domain vocabulary;
• expected acronyms;
• casing preferences;
• prior segment tail for continuity;
• language variety when relevant.

Example:

This is an English product interview about Aster Lane, SOC 2, Snowflake, Zendesk, and call summarization. Preserve product names. Use "Aster Lane" not "Asterlane". Use "SOC 2" not "sock two" in the transcript.

Complete example: diarized meeting transcript

User intent: transcribe a 35-minute customer interview and preserve speaker turns for a researcher.

Model and endpoint: /v1/audio/transcriptions, gpt-4o-transcribe-diarize, diarized_json.

Python example:

from pathlib import Path
from openai import OpenAI

client = OpenAI()

audio_path = Path("raw/customer-interview-2026-07-10.m4a")  # Prevalidated at <25 MB.

with audio_path.open("rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="gpt-4o-transcribe-diarize",
        file=audio_file,
        response_format="diarized_json",
        chunking_strategy="auto",
    )

Path("artifacts/transcripts/customer-interview-diarized-v001.json").write_text(
    transcript.model_dump_json(indent=2),
    encoding="utf-8",
)

Expected result:

• diarized JSON with speaker-labeled segments;
• enough structure for qualitative coding;
• no word-level caption timing unless separately produced.

Likely failure modes:

• overlapping speakers may be mislabeled;
• a recurring product name may still be misspelled;
• diarization should not be treated as legal identity verification.

Repair:

• manually map speaker labels to real names after review;
• transcribe unclear spans separately with a prompt-capable model and a corrected vocabulary list, then reconcile them with the diarized timeline;
• keep the raw transcript and reviewed transcript as separate artifacts.

Complete example: caption timing with word timestamps

User intent: create caption timing for a short tutorial clip where word-level karaoke highlighting is required.

Model and endpoint: /v1/audio/transcriptions, whisper-1, verbose_json, timestamp_granularities=["word"].

Python example:

from pathlib import Path
from openai import OpenAI

client = OpenAI()

with open("exports/tutorial-voice.wav", "rb") as audio_file:
    transcription = client.audio.transcriptions.create(
        model="whisper-1",
        file=audio_file,
        response_format="verbose_json",
        timestamp_granularities=["word"],
        prompt=(
            "Tutorial narration about Aster Lane, SOC 2, and Zendesk. "
            "Use product capitalization exactly."
        ),
    )

Path("artifacts/captions/tutorial-word-timestamps-v001.json").write_text(
    transcription.model_dump_json(indent=2),
    encoding="utf-8",
)

Why this route:

• The documented timestamp granularities route is tied to whisper-1.
• Word timestamps add latency, so request them only when the edit actually needs word-level timing.
• The transcript still needs a human or automated caption QA pass for line breaks, reading speed, and accessibility.

Translation to English

The audio translations endpoint translates audio into English text. Do not present it as multilingual dubbing, voice conversion, or localized audio production.

Use it when:

• the user has non-English speech and wants an English transcript;
• the downstream edit, research, or subtitle workflow is English-first;
• the user accepts loss of original-language phrasing and speaker performance nuance.

Do not use it when:

• the deliverable needs subtitles in the original language;
• the target language is not English;
• the user wants synthetic dubbed speech in another language;
• speaker identity or exact legal wording matters without human review.

Complete example:

from pathlib import Path
from openai import OpenAI

client = OpenAI()

with open("raw/founder-clip-es.m4a", "rb") as audio_file:
    translation = client.audio.translations.create(
        model="whisper-1",
        file=audio_file,
        response_format="text",
        prompt=(
            "The speaker discusses Aster Lane, customer support analytics, "
            "and SOC 2. Translate to clear English while preserving product names."
        ),
    )

Path("artifacts/transcripts/founder-clip-english-v001.txt").write_text(
    translation,
    encoding="utf-8",
)

Approval rule: if the translation will be published, require bilingual review or explicit user acceptance that the machine translation may miss nuance.

Audio-capable chat completions

Use Chat Completions with an audio-capable model when audio is part of a conversational model turn, not merely a file conversion job. Documented pattern: use gpt-audio-1.5, set modalities to include audio, provide audio output parameters, and include audio inputs as base64 input_audio content blocks. Source: https://developers.openai.com/api/docs/guides/audio

Use this route for:

• “listen to this short recording and answer out loud”;
• voice-enabled chat in an existing Chat Completions app;
• an agent that reasons about spoken content and returns both text and audio in one bounded turn.

Do not use this route for:

• bulk transcription, where /v1/audio/transcriptions is simpler;
• pure narration from a finished script, where /v1/audio/speech is simpler;
• continuous microphone interaction, where realtime sessions fit better.

Complete example:

import base64
from pathlib import Path
from openai import OpenAI

client = OpenAI()

audio_bytes = Path("raw/user-question.wav").read_bytes()
encoded_audio = base64.b64encode(audio_bytes).decode("utf-8")

completion = client.chat.completions.create(
    model="gpt-audio-1.5",
    modalities=["text", "audio"],
    audio={"voice": "alloy", "format": "wav"},
    messages=[
        {
            "role": "system",
            "content": (
                "Answer as a concise support coach. If the recording is unclear, "
                "say what you could not determine rather than guessing."
            ),
        },
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is the customer asking for?"},
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": encoded_audio,
                        "format": "wav",
                    },
                },
            ],
        },
    ],
)

message = completion.choices[0].message
Path("artifacts/chat-audio/answer-transcript-v001.txt").write_text(
    message.audio.transcript,
    encoding="utf-8",
)
Path("artifacts/chat-audio/answer-v001.wav").write_bytes(
    base64.b64decode(message.audio.data)
)

Custody note: chat audio responses include base64 audio bytes and a transcript. Store both if the output is an approved production artifact. If privacy policy requires no server-side persistence, verify store behavior and the organization’s data controls before use.

Latency, cost, and batching

Documented cost facts are volatile; always re-check pricing on the day of production. On 2026-07-10, OpenAI’s pricing page lists:

• realtime/audio-generation model prices by audio/text/image token or minute depending on model;
• transcription model prices for gpt-4o-transcribe and gpt-4o-mini-transcribe, including estimated per-minute rates;
• no separate platform fee for Responses, Chat Completions, Realtime, Batch, or Assistants APIs beyond model/tool billing. Source: https://developers.openai.com/api/docs/pricing

Production heuristics:

• Estimate TTS cost from generated text and audio-token billing when using audio-generation models; estimate transcription cost from audio duration and the current pricing page.
• For transcription, run low-cost first-pass transcription only when errors will be caught later; do not use rough transcripts as final legal, medical, compliance, or public quote artifacts.
• For long TTS scripts, generate auditions first, then batch full narration only after voice approval.
• For long transcription files, split only on natural pauses or scene boundaries if you need parallelism or retry isolation. Keep overlap small and reconcile duplicates in post.
• Streaming is useful for UX latency; it does not remove the need to store final artifacts and review output before publication.

Rights, consent, privacy, and safety

Hard requirements:

• Do not generate, transform, or imitate a person’s voice in a way that could confuse authenticity unless the user has consent and rights. OpenAI policy specifically flags use of someone’s likeness, including voice, without consent in confusing contexts. Source: https://openai.com/policies/usage-policies/
• Do not present a synthetic voice as a real person’s live or recorded speech.
• Do not process private calls, interviews, meetings, or voicemails unless the user has lawful authority and any required participant consent.
• Clearly disclose to end users that OpenAI TTS audio is AI-generated and not a human voice; also satisfy any additional legal, platform, or client disclosure requirements.
• For minors, healthcare, legal, workplace surveillance, biometric, or sensitive personal data, pause for a privacy and policy review before processing.
• Treat diarization as a production convenience, not identity proof.
• Treat transcripts as potentially sensitive records. Store raw audio and transcripts only in approved locations.

Data-control facts verified 2026-07-10:

• OpenAI’s platform data-controls table says API data is not used for training across listed endpoints.
• The table lists /v1/audio/transcriptions and /v1/audio/translations with no abuse-monitoring retention and no application-state retention.
• The table lists /v1/audio/speech with 30-day abuse-monitoring retention and no application-state retention.
• Audio outputs in Chat Completions and Responses may have application state stored for 1 hour to enable multi-turn conversations.
• Zero Data Retention and Modified Abuse Monitoring require approval and have endpoint-specific limitations. Source: https://developers.openai.com/api/docs/guides/your-data

Approval and artifact custody protocol

Use this protocol for any production asset, public-facing content, customer content, regulated transcript, or paid batch job.

Before generation or transcription:

1. Name the endpoint, model, output format, and reason for the route.
2. Confirm rights/consent for voices, recordings, scripts, brands, and music beds.
3. Confirm privacy classification and storage location.
4. Confirm cost estimate source and date if cost is material.
5. Confirm whether the first call is an audition/sample or full batch.

After a sample:

1. Store raw output under a versioned path.
2. Produce a short review note: model, voice, prompt/instructions, input file/script, format, reviewer, date, pass/fail.
3. Ask for approval or revision before generating the full batch.

After a full batch:

1. Store raw inputs, generated outputs, transcripts, sidecars, manifests, and approvals separately.
2. Preserve original model outputs before post-processing.
3. Write a manifest that records model, endpoint, parameters, source files, output files, checksums, and approval status.
4. Mark derivative files clearly: edited, normalized, mixed, captioned, translated, or reviewed.
5. Do not delete raw source or approved masters unless the user’s retention policy requires deletion and the deletion is logged.

Minimal artifact manifest shape:

{
  "artifact_id": "narration_sc01_v003",
  "created_at": "2026-07-10T18:30:00Z",
  "provider": "openai",
  "endpoint": "/v1/audio/speech",
  "model": "gpt-4o-mini-tts",
  "voice": "marin",
  "input_script": "scripts/narration/sc01_v004.txt",
  "instructions": "Warm, credible, medium pace; pronounce Aster as ASS-ter.",
  "parameters": {
    "response_format": "wav",
    "speed": 1.0
  },
  "outputs": [
    {
      "path": "artifacts/audio/sc01_v003.wav",
      "role": "raw_model_master",
      "sha256": "..."
    }
  ],
  "approval": {
    "status": "approved",
    "approver": "user",
    "approved_at": "2026-07-10T18:45:00Z"
  },
  "source_verification": {
    "docs_checked_at": "2026-07-10",
    "pricing_checked_at": "2026-07-10"
  }
}

Quality review

For generated speech, review:

• pronunciation of names, acronyms, numbers, currencies, and URLs;
• pacing, pauses, and breath realism;
• emotion relative to the brief;
• consistency across chunks;
• clipping, dropouts, artifacts, noise, or abrupt endings;
• compliance with consent/disclosure requirements;
• loudness and peak headroom after post-processing.

For transcripts, review:

• speaker labels and turn boundaries;
• named entities and technical terms;
• timestamps against the actual waveform;
• omissions during crosstalk, music, applause, or noise;
• hallucinated text in inaudible sections;
• punctuation and casing;
• privacy redaction requirements.

For translated transcripts, review:

• whether the output is English-only as expected;
• product names preserved;
• idioms and jokes not over-literalized;
• quotes not published without bilingual review;
• culturally sensitive or legal wording escalated for human review.

Common failure modes and repairs

Final response pattern for production work

When handing off results, include:

• endpoint and model used;
• input source and output artifact paths;
• parameters that materially affected output;
• approval status;
• QA findings and unresolved risks;
• any required human review before publication;
• source dates for volatile pricing/model/retention claims.