The Magic of AI Text Polishing: How OpenTypeless Transforms Speech into Clean Text

Raw speech-to-text output is messy. It lacks punctuation, has grammar issues, includes filler words like 'um' and 'like', and often miscapitalizes technical terms. This is true regardless of which STT provider you use — even the best ones produce output that needs cleanup. OpenTypeless's AI polishing step is what transforms this raw output into clean, professional text that's ready to use.

What is AI Text Polishing?

AI text polishing is the process of using a large language model (LLM) to clean up raw transcription output. Think of it as having a skilled editor review every sentence you dictate — fixing grammar, adding punctuation, removing verbal tics, and formatting the text naturally. The key difference from traditional grammar checkers is that LLMs understand context and intent, so they can make intelligent decisions about what to fix and what to preserve.

In OpenTypeless, polishing happens automatically after every transcription. Your speech goes through the STT provider first, then the raw text is sent to your chosen LLM with a carefully crafted system prompt. The LLM returns polished text that preserves your original meaning while cleaning up the delivery. The entire process adds only 200-500ms to the pipeline — barely noticeable in practice.

How the Pipeline Works

The polishing pipeline is designed to be fast and reliable. After the STT provider returns raw text, OpenTypeless constructs a prompt that includes: the raw transcription, your custom dictionary terms (if any), and instructions for the LLM on how to polish the text. The prompt is carefully tuned to preserve meaning, maintain tone, and handle edge cases like code snippets, URLs, and technical terminology.

The system prompt tells the LLM to: fix grammar and punctuation errors, remove filler words and verbal hesitations, capitalize proper nouns and technical terms correctly, maintain the speaker's original tone and intent, preserve any code or technical content exactly as spoken, and apply custom dictionary corrections. This prompt has been refined through thousands of real-world transcriptions to handle the most common speech patterns.

Before and After Examples

The best way to understand AI polishing is to see it in action. Here are real examples from OpenTypeless users showing the transformation from raw speech to polished text.

Example 1: Technical Discussion

Raw: 'so basically i think we should like implement the new feature using react server components and maybe add some caching you know and uh also we need to think about the database schema i guess'. Polished: 'I think we should implement the new feature using React Server Components and add caching. We also need to consider the database schema.' The filler words are gone, capitalization is correct, and the text reads naturally.

Example 2: Email Draft

Raw: 'hey can you send me the report from last week i need it for the meeting tomorrow morning also let me know if the budget numbers have been updated thanks'. Polished: 'Hey, can you send me the report from last week? I need it for tomorrow morning's meeting. Also, let me know if the budget numbers have been updated. Thanks!' Punctuation is added, sentences are properly separated, and the casual tone is preserved.

Example 3: Code Review Comment

Raw: 'this function should probably use typescript generics instead of any types and we should add error handling for the api call also the variable name user data should be camel case'. Polished: 'This function should use TypeScript generics instead of `any` types. We should add error handling for the API call. Also, the variable name `userData` should be camelCase.' Technical terms are formatted correctly, and code references are properly marked.

Choosing an LLM Provider

OpenTypeless supports 11 LLM providers for text polishing, each with different trade-offs between speed, quality, and cost. The choice of LLM affects how natural and accurate the polished output feels. Here's a breakdown of the most popular options.

For Speed: Groq

Groq runs open-source models like Llama on custom LPU hardware, delivering responses in under 100 milliseconds. For voice input where every millisecond counts, Groq makes the polishing step feel instant. The quality is good — not quite at GPT-4o level, but more than adequate for cleaning up speech transcriptions. Groq is the default recommendation for users who prioritize responsiveness.

For Quality: OpenAI GPT-4o or Claude

If you want the most natural, human-sounding polished text, OpenAI GPT-4o and Claude produce the best results. They handle nuance, tone preservation, and complex sentence restructuring better than smaller models. The trade-off is slightly higher latency (300-800ms) and higher per-token cost. For professional writing, emails, and documents where quality matters most, these are the top choices.

For Cost: DeepSeek

DeepSeek offers excellent polishing quality at a fraction of the cost of OpenAI or Claude. Their models are particularly strong at technical content and code-related text. If you're a heavy voice input user processing thousands of words per day, DeepSeek's pricing makes it the most economical choice without sacrificing much quality.

For Privacy: Ollama

Ollama runs LLMs entirely on your local machine — no data leaves your computer. This is the ultimate privacy option, ideal for sensitive content like medical notes, legal documents, or proprietary code discussions. The trade-off is that local models are slower and less capable than cloud-hosted ones, but for basic text cleanup they work well. You'll need a machine with at least 8GB of RAM and a decent GPU for smooth performance.

Custom Dictionary

The custom dictionary is one of OpenTypeless's most powerful features for technical users. When you add terms to your dictionary, the LLM knows to preserve them exactly as spelled during polishing. This means 'kubernetes' becomes 'Kubernetes', 'postgres' becomes 'PostgreSQL', 'nextjs' becomes 'Next.js', and your company's product names are always capitalized correctly. The dictionary works across all LLM providers and dramatically improves the accuracy of technical transcriptions.