Prompting

Use prompt engineering to control transcription style and improve accuracy for domain-specific terminology. This guide documents best practices for crafting effective prompts for Universal-3-Pro speech transcription.

How prompting works

Universal-3-Pro is a Speech-augmented Large Language Model (SpeechLLM). The architecture is a multi-modal LLM with an audio encoder and LLM decoder designed to understand and process speech, audio, and text inputs in the same workflow.

SpeechLLM prompting works more like selecting modes and knobs than open-ended instruction following. The model is trained primarily to transcribe, then fine-tuned to respond to common transcription instructions for style, speakers, and speech events.

What prompts can do

What prompts cannot reliably do

For consistent proper-noun accuracy or domain vocabulary, you’ll usually need context injection into the prompt, use keyterms prompt to highlight known keyterms, or a post-processing LLM correction step.

Getting started with prompt examples

Basic transcription

Sample prompt 1: Simple, readable transcription

Transcribe this audio

Characteristics of prompt output:

• Without much instruction, the model follows its training data to transcribe a human readable transcript
• Audio is transcribed as a human would read it, not including disfluencies, false starts, hesitations, and other speech patterns
• No extra weight is given to contextual accuracy of keyterms or in-domain data (i.e. medical), resulting in strong overall WER but higher error rate on rare words

Useful for:

• General transcription
• Readability and sentence structure
• Balanced WER vs contextual accuracy

Enhanced transcription

Sample prompt 2: Enhanced speech patterns and entity accuracy

Transcribe accurately with attention to proper nouns, technical terms,
and natural speech patterns.

Characteristics of prompt output:

• With more instruction, the model attempts to add contextual correctness to rare words and entities
• Audio is transcribed with more natural speech patterns, including basic disfluencies, false starts, hesitations, and other speech patterns
• Extra weight is given to contextual accuracy of keyterms or in-domain data (i.e. medical), resulting in small degradation of WER for lower error rate on rare words

Useful for:

• In-domain transcription where you know the general context (i.e. medical, legal, finance, technical)
• Basic capture of speech patterns
• Higher WER in exchange for contextual accuracy

Maximum verbatim transcription

Sample prompt 3: Maximum speech patterns, context, and multilingual accuracy

Required: Preserve the original language(s) and script as spoken,
including code-switching and mixed-language phrases.
Mandatory: Preserve linguistic speech patterns including disfluencies,
filler words, hesitations, repetitions, stutters, false starts, and
colloquialisms in the spoken language.
Always: Transcribe speech with your best guess based on context in all
possible scenarios where speech is present in the audio.

Characteristics of prompt output:

• With many instructions, the model attempts to become as verbatim as possible across a number of different domains - speech patterns, contextual correctness, entity accuracy, and code-switching/multilingual transcription.
• Languages are described as spoken without translation, preserving the meaning of natural code-switching and mixed-language phrases.
• Audio is transcribed with as many natural speech patterns as possible, including all potential disfluencies, filler words, hesitations, repetitions, stutters, false starts, and colloquialisms in the original language.
• Because the model is searching for all speech patterns, prompts like this are more likely to also pick up things like cross talk or background noise or low volume audio
• Extra weight is given to contextual accuracy by telling the model to guess at different words based on the context surrounding that word.

Useful for:

• Maximum capture of speech patterns - will generate the most words transcribed
• Higher WER in exchange for contextual accuracy and multilingual accuracy
• Across a variety of transcripts where you think these different patterns will be encountered

System prompts

Sample prompt 3 is the current system prompt as of February 20, 2026. If you are iterating on prompts and trying different styles, this is the prompt you can start experimenting with and removing/adding instructions to compare to production results.

To be explicit, the system prompt is:

Required: Preserve the original language(s) and script as spoken,
including code-switching and mixed-language phrases.
Mandatory: Preserve linguistic speech patterns including disfluencies,
filler words, hesitations, repetitions, stutters, false starts, and
colloquialisms in the spoken language.
Always: Transcribe speech with your best guess based on context in all
possible scenarios where speech is present in the audio.

This has been chosen as the system prompt because it has the best balance between being maximum verbatim, capturing mixed language speech, and transcribing audio under challenging conditions.

Prior to February 20, 2026 the system prompt was:

Transcribe this audio

Prompt capabilities

Each capability below acts as a “knob” you can turn. Combine 3-6 capabilities maximum for best results.

1. Verbatim transcription and disfluencies

What it does: Preserves natural speech patterns including filler words, false starts, repetitions, and self-corrections.

Reliability: High

Example prompts:

Include spoken filler words, hesitations, plus repetitions and false starts when clearly spoken.

Preserve all disfluencies exactly as spoken including verbal hesitations,
restarts, and self-corrections.

Transcribe verbatim:
- Filler words: yes
- Repetitions: yes
- Stutters: yes
- False starts: yes
- Colloquial: yes

2. Output style and formatting

What it does: Controls punctuation, capitalization, and readability without changing words.

Reliability: High

Example prompts:

Transcribe this audio with beautiful punctuation and formatting.

Use expressive punctuation to reflect emotion and prosody.

Use standard punctuation and sentence breaks for readability.

3. Context aware clues

What it does: Helps with jargon, names, and domain expectations that are known from the audio file.

Reliability: Medium

Example prompts:

Transcribe this audio. This is a doctor-patient visit, prioritize accurately transcribing medications and diseases wherever possible.

Prioritize transcribing this virtual meeting like a human transcritionist, paying close attention to names, company names, jargon, acronyms, and other entities.

4. Entity accuracy and spelling

What it does: Improves accuracy for proper nouns, brands, technical terms, and domain vocabulary.

Reliability: Medium

Example prompts:

Use standard spelling and the most contextually correct spelling of all words
including names, brands, drug names, medical terms, and proper nouns.

Non-negotiable: Pharmaceutical accuracy required across all medications and drug names

Preserve acronyms and capitalization of company names and legal entities

The model works best here when you tell it the pattern of entities to identify and how you wish for it to consider those entities as it transcribes speech.

5. Speaker attribution

What it does: Marks speaker turns and adds identifying labels.

Reliability: Experimental (YMMV)

Example prompts:

Transcribe the audio verbatim, include speaker change markers.

Tag speaker changes with context like name, role, or gender based on speech content.

Label speakers by role when identifiable (doctor, patient).

Speaker labels can be tagged with names, roles, genders, and more from the audio file. Simply add the desired category for the labels into your prompt.

6. Audio event tags

What it does: Marks non-speech sounds like music, laughter, applause, and background noise.

Reliability: Experimental (YMMV)

Example prompts:

Preserve non-speech audio in tags to indicate when the audio occurred.

Tag sounds: [laughter], [silence], [noise], [cough], [sigh].

Include audio event markers for music, laughter, and applause.

7. Code-switching and multilingual

What it does: Handles audio where speakers switch between languages.

Reliability: Medium

Example prompts:

Transcribe in the original language mix (code-switching), preserving words
in the language they are spoken.

Preserve natural code-switching between English and Spanish. Retain spoken language as-is with mixed language words.

8. Numbers and measurements

What it does: Controls how numbers, percentages, and measurements are formatted.

Reliability: Medium

Example prompts:

Convert spoken numbers to digits.

Use digits for numbers, percentages, and measurements.

Format financial figures with standard notation and format numbers for maximum readability.

9. Difficult audio handling

What it does: Controls how the model handles uncertain or unclear audio segments. You can choose between two opposite strategies: maximizing guesses or flagging uncertainty.

Reliability: Experimental (YMMV)

Strategy 1: Maximize guesses

Tell the model to always attempt a transcription, even when confidence is low:

Always transcribe speech with your best guess based on context in all possible scenarios where speech is present in the audio.

This is useful when you want the most complete transcript possible and plan to verify accuracy downstream.

Strategy 2: Flag uncertainty

Tell the model to mark segments it is unsure about instead of guessing:

Transcribe speech with your best guess when speech is heard, mark [unclear] when audio segments are unknown.

If unintelligible, write [unclear]. Mark inaudible segments.

This is useful for quality-sensitive workflows where incorrect guesses are worse than gaps.

Combining both strategies:

You can run the same audio file with both strategies to create a powerful review workflow. The “best guess” transcript gives you the most complete output, while the “flag uncertainty” transcript highlights exactly which segments need human review.

10. PII redaction

What it does: Tags personal identifiable information such as names, addresses, and contact details within the transcript.

Reliability: Experimental (YMMV)

Example prompts:

Tag all personal information as [private] including names, addresses, phone numbers, email addresses, and account numbers.

Mandatory: Identify and tag all personally identifiable information as [private]. This includes full names, physical addresses, contact information, social security numbers, and financial account numbers.

Be specific about which types of PII you want tagged. A vague prompt like redact PII may not give the model enough guidance. Enumerate the categories you care about.

Best practices

What helps

What hurts

Prompting vs. keyterms prompting

Universal-3-Pro supports two methods for improving transcription accuracy: open-ended prompting (the prompt parameter) and keyterms prompting (the keyterms_prompt parameter).

Prompt generator

This prompt generator helps you create a starting prompt based on your selected transcription style. Paste a sample of your transcript and select your preferred style to get a customized prompt recommendation.

Prompt library

Browse community-submitted prompts, vote on the ones that work best, and share your own.

Domain-specific sample prompts

Legal transcription

Best for: Court proceedings, depositions, legal hearings

Mandatory: Transcribe legal proceedings with precise terminology intact.
Required: Preserve linguistic speech patterns including disfluencies, filler
words, hesitations, repetitions, stutters, false starts, and colloquialisms in
the spoken language.
Non-negotiable: Distinguish between speakers through clear role-based attribution
(Judge:, Witness:, Counsel: over generic labels).
Label participants by role when identifiable (judge, counsel, witness).

Why it works: Combines authoritative language (Mandatory, Required, Non-negotiable), clear disfluency instructions, speaker attribution guidance, and domain terminology.

Medical transcription

Best for: Clinical documentation, medical dictation, patient-provider conversations

Mandatory: Preserve all clinical terminology exactly as spoken including
drug names, dosages, and diagnostic terms.
Required: Preserve linguistic speech patterns including disfluencies, filler
words, hesitations, repetitions, stutters, false starts, and colloquialisms in
the spoken language.
Label physician and patient speech clearly when identifiable.

Why it works: Combines authoritative language (Mandatory, Required) with clear disfluency instructions, while ensuring clinical terminology accuracy and clear speaker attribution for medical documentation.

Financial/Earnings calls

Best for: Quarterly earnings calls, investor presentations, financial meetings

Mandatory: Transcribe this corporate earnings call with precise financial
terminology.
Required: Preserve linguistic speech patterns including disfluencies, filler
words, hesitations, repetitions, stutters, false starts, and colloquialisms in
the spoken language.
Non-negotiable: Financial term accuracy across all financial terminology,
acronyms, and industry-standard phrases.
Format numerical data with standard notation. Label executives and speakers
by role when identifiable (CEO, CFO, Analyst).

Why it works: Balances financial terminology precision with verbatim capture of speech patterns, listing specific financial terms for domain accuracy.

Software/Technical meetings

Best for: Engineering standups, code reviews, technical discussions

Mandatory: Transcribe this technical meeting with multiple participants.
Required: Preserve linguistic speech patterns including disfluencies, filler
words, hesitations, repetitions, stutters, false starts, and colloquialisms in
the spoken language.
Non-negotiable: Technical terminology accuracy across all software names,
frameworks, and industry acronyms.
Mark transitions between participants explicitly. Capture self-corrections
and restarts from speech.

Why it works: Preserves natural developer speech patterns while listing specific technical terms for domain accuracy.

Code-switching (Bilingual)

Best for: Multilingual conversations, Spanglish, language mixing

Mandatory: Transcribe verbatim, preserving natural code-switching between
English and Spanish.
Required: Retain spoken language as-is without translation. Preserve words
in the language they are spoken.
Non-negotiable: Preserve linguistic speech patterns including disfluencies,
filler words, hesitations, repetitions, stutters, false starts, and
colloquialisms in the spoken language.
Resolve sound-alike errors using bilingual context for maximum accuracy.

Why it works: Explicitly instructs preservation over translation, handles cross-language disfluencies, and uses pattern-based accuracy guidance for bilingual context.

Customer support call

Best for: Contact center calls, customer service interactions, agent-customer conversations

Context: a customer support call. Prioritize accurately transcribing names,
account details, and balance amounts.
Mandatory: Transcribe any overlapping speech across channels including crosstalk.
Required: Pay attention to proper nouns like names, balance amounts, and bank name
being correct.
Non-negotiable: Preserve linguistic speech patterns including disfluencies,
filler words, hesitations, repetitions, stutters, false starts, and
colloquialisms in the spoken language.

Why it works: Combines domain context with actionable instructions for entity accuracy, multichannel awareness for overlapping speech, and verbatim speech preservation for quality assurance and compliance review.

How to build your prompt

Step 1: Start with your base need

Choose your primary transcription goal:

Step 2: Add authoritative language

Prefix each instruction with:

• Non-negotiable:
• Mandatory:
• Required:
• Strict requirement:

Step 3: Add instructions one by one

We recommend you layer on each instruction one by one to see the impact on the transcription output. Since conflicting instructions can cause outputs to degrade, adding each instruction one by one allows you to test and evaluate how each instruction improves/degrades your transcription output.

Step 4: Iterate and test

1. Identify target terms - What words/phrases are being transcribed incorrectly?
2. Find the error pattern - Vowel substitution? Sound-alike? Phonetic spelling?
3. Choose example terms - Pick 2-3 common terms with the SAME error pattern
4. Test and verify - Listen to the audio to confirm correctness
5. Measure success rate - Test variations on sample files

Prompt Repair Wizard: If you need help iterating on your prompts, try the Prompt Repair Wizard on the dashboard. Paste your current prompt, describe the issues you’re seeing in the output, and it will suggest improvements based on prompting best practices.

Evaluating transcription accuracy

When evaluating Universal-3-Pro output against human-labeled ground truth files, be aware that the model frequently outperforms human transcribers. If your word error rate (WER) evaluation shows unexpected insertions from Universal-3-Pro, listen back to the original audio before assuming the model is wrong. In many cases, the model is correctly transcribing audio that a human transcriber missed or normalized.

Tips for accurate evaluation:

• Use the [unclear] tag in your evaluation prompt to prevent the model from guessing on audio that a human transcriber would also miss. This improves WER alignment.
• Review insertions manually by listening to the audio at the flagged timestamps. Many apparent errors are actually the model being more accurate.
• Consider Semantic WER or other evaluation metrics over traditional normalized WER for a more nuanced evaluation that accounts for these differences.

Need help?

Prompt engineering is a new and evolving concept with SpeechLLM models. If you need help generating a prompt, our Engineering team is happy to support. Feel free to open a new live chat or send an email in the widget in the bottom right hand corner (more contact info here).