> ## Documentation Index > Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Prompting and Keyterms export const ModelBadges = ({models}) => { return
{models.map(model => {model} )}
; }; export const AudioPlayer = ({src, type = "audio/mpeg", fallbackText = "Your browser does not support the audio element."}) => { return ; }; Universal-3.5 Pro is highly accurate out of the box, but for challenging audio like short clips with limited context, noisy environments, or audio with niche references, you can give the model information about your audio to improve transcription accuracy. There are two ways to give the model information about your audio: * **Contextual prompting** (`prompt`) — a natural-language description of what the audio is about: the domain, the scenario, or the full details of the conversation. * **Keyterms prompting** (`keyterms_prompt`) — an explicit list of terms you want the model to recognize accurately. ## Contextual prompting Use the `prompt` parameter to provide context about your audio — describe what is being transcribed, not how to transcribe it. Formatting and behavioral instructions are ignored. The model stays grounded in the audio, so irrelevant context won't cause hallucinated words. For example, this is a 2-second clip from a League of Legends pro interview: Without prompt: ```txt theme={null} And so look who I've been a dear. ``` With prompt: ```txt theme={null} In solo queue, I ban Azir. ``` ```python {11} expandable theme={null} import requests import time base_url = "https://api.assemblyai.com" headers = {"authorization": ""} data = { "audio_url": "https://assembly.ai/prompt-8", "language_detection": True, "prompt": "League of Legends roles" } response = requests.post(base_url + "/v2/transcript", headers=headers, json=data) if response.status_code != 200: print(f"Error: {response.status_code}, Response: {response.text}") response.raise_for_status() transcript_response = response.json() transcript_id = transcript_response["id"] polling_endpoint = f"{base_url}/v2/transcript/{transcript_id}" while True: transcript = requests.get(polling_endpoint, headers=headers).json() if transcript["status"] == "completed": print(transcript["text"]) break elif transcript["status"] == "error": raise RuntimeError(f"Transcription failed: {transcript['error']}") else: time.sleep(3) ``` ```python {10} expandable theme={null} import assemblyai as aai aai.settings.api_key = "" audio_file = "https://assembly.ai/prompt-8" config = aai.TranscriptionConfig( language_detection=True, prompt="League of Legends roles", ) transcript = aai.Transcriber().transcribe(audio_file, config) print(transcript.text) ``` ```javascript {10} expandable theme={null} const baseUrl = "https://api.assemblyai.com"; const headers = { authorization: "", }; const data = { audio_url: "https://assembly.ai/prompt-8", language_detection: true, prompt: "League of Legends roles", }; const url = `${baseUrl}/v2/transcript`; let res = await fetch(url, { method: "POST", headers: { ...headers, "Content-Type": "application/json" }, body: JSON.stringify(data), }); if (!res.ok) throw new Error(`Error: ${res.status}`); const response = await res.json(); const transcriptId = response.id; const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`; while (true) { res = await fetch(pollingEndpoint, { headers }); if (!res.ok) throw new Error(`Error: ${res.status}`); const transcriptionResult = await res.json(); if (transcriptionResult.status === "completed") { console.log(transcriptionResult.text); break; } else if (transcriptionResult.status === "error") { throw new Error(`Transcription failed: ${transcriptionResult.error}`); } else { await new Promise((resolve) => setTimeout(resolve, 3000)); } } ``` ```javascript {13} expandable theme={null} import { AssemblyAI } from "assemblyai"; const client = new AssemblyAI({ apiKey: "", }); const audioFile = "https://assembly.ai/prompt-8"; const params = { audio: audioFile, language_detection: true, prompt: "League of Legends roles", }; const run = async () => { const transcript = await client.transcripts.transcribe(params); console.log(transcript.text); }; run(); ``` ### Prompting guide Contextual prompts work at three levels of specificity. Use the least specific level that covers your use case, and add detail when your audio contains uncommon names or terms the model can't otherwise know. | Level | Length | What it contains | Example | | ------------ | ----------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Domain** | 2–5 words | The domain only | `Medical consultation call.` | | **Scenario** | 5–15 words | What the conversation is about | `Cardiology consultation about chest pain symptoms.` | | **Detailed** | 20–50 words | Full description, including names, products, or identifiers | `Cardiology consultation between Dr. Smith and an elderly patient regarding recurring chest pain, ECG results, and medication adjustment for hypertension.` | Guidelines for writing contextual prompts: * Write plain, complete sentences that describe the audio * Keep it to one short block of text. Don't pack lists of keywords into the contextual prompt ## Keyterms prompting Keyterms prompting allows you to provide up to 1,000 words or phrases (maximum 6 words per phrase) using the `keyterms_prompt` parameter to improve transcription accuracy for those terms and related variations or contextually similar phrases. Here is an example showing how you can use keyterms prompting to improve transcription accuracy for a name with distinctive spelling and formatting. Without keyterms prompting: ```txt theme={null} Hi, this is Kelly Byrne Donahue ``` With keyterms prompting: ```txt theme={null} Hi, this is Kelly Byrne-Donoghue ``` ```python {10} expandable theme={null} import requests import time base_url = "https://api.assemblyai.com" headers = {"authorization": ""} data = { "audio_url": "https://assemblyaiassets.com/audios/keyterms_prompting.wav", "language_detection": True, "keyterms_prompt": ["Kelly Byrne-Donoghue"] } response = requests.post(base_url + "/v2/transcript", headers=headers, json=data) if response.status_code != 200: print(f"Error: {response.status_code}, Response: {response.text}") response.raise_for_status() transcript_response = response.json() transcript_id = transcript_response["id"] polling_endpoint = f"{base_url}/v2/transcript/{transcript_id}" while True: transcript = requests.get(polling_endpoint, headers=headers).json() if transcript["status"] == "completed": print(transcript["text"]) break elif transcript["status"] == "error": raise RuntimeError(f"Transcription failed: {transcript['error']}") else: time.sleep(3) ``` ```javascript {11} expandable theme={null} const baseUrl = "https://api.assemblyai.com"; const headers = { authorization: "", }; const data = { audio_url: "https://assemblyaiassets.com/audios/keyterms_prompting.wav", language_detection: true, keyterms_prompt: ["Kelly Byrne-Donoghue"], }; const url = `${baseUrl}/v2/transcript`; let res = await fetch(url, { method: "POST", headers: { ...headers, "Content-Type": "application/json" }, body: JSON.stringify(data), }); if (!res.ok) throw new Error(`Error: ${res.status}`); const response = await res.json(); const transcriptId = response.id; const pollingEndpoint = `${baseUrl}/v2/transcript/${transcriptId}`; while (true) { res = await fetch(pollingEndpoint, { headers }); if (!res.ok) throw new Error(`Error: ${res.status}`); const transcriptionResult = await res.json(); if (transcriptionResult.status === "completed") { console.log(transcriptionResult.text); break; } else if (transcriptionResult.status === "error") { throw new Error(`Transcription failed: ${transcriptionResult.error}`); } else { await new Promise((resolve) => setTimeout(resolve, 3000)); } } ``` ```python {9} theme={null} import assemblyai as aai aai.settings.api_key = "" audio_file = "https://assemblyaiassets.com/audios/keyterms_prompting.wav" config = aai.TranscriptionConfig( language_detection=True, keyterms_prompt=["Kelly Byrne-Donoghue"] ) transcript = aai.Transcriber(config=config).transcribe(audio_file) print(transcript.text) ``` ```javascript {12} theme={null} import { AssemblyAI } from "assemblyai"; const client = new AssemblyAI({ apiKey: "", }); const audioFile = "https://assemblyaiassets.com/audios/keyterms_prompting.wav"; const params = { audio: audioFile, language_detection: true, keyterms_prompt: ["Kelly Byrne-Donoghue"], }; const transcript = await client.transcripts.transcribe(params); console.log(transcript.text); ``` **Keyword count limits** While we support up to 1000 key words and phrases, actual capacity may be lower due to internal tokenization and implementation constraints. Key points to remember: * Each word in a multi-word phrase counts towards the 1000 keyword limit * Capitalization affects capacity (uppercase tokens consume more than lowercase) * Longer words consume more capacity than shorter words For optimal results, use shorter phrases when possible and be mindful of your total token count when approaching the keyword limit. ## Need help? If you'd like help building or optimizing a prompt for your audio, our team can help: open a live chat or email us via the widget in the bottom-right corner ([contact info](https://www.assemblyai.com/contact/support)).