{"slug": "using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand", "title": "Using an LLM to automate a task that used to take hours by hand", "summary": "The article describes how the author automated the manual process of aligning source and translated audio phrases for latency measurement in live speech-to-speech translation. By using an LLM to handle semantic alignment across languages—a task that previously required hours of human listening and timestamp logging—the process now takes only a few minutes. The author emphasizes that this pattern applies broadly: any workflow step where a human compares two pieces of information to find correspondences can likely be automated with an LLM.", "body_md": "I want to share a concrete example of using an LLM to automate a manual process in my workflow. Not chatbot stuff. An actual pipeline step that used to require a human sitting with two audio tracks for hours.\nI build live speech-to-speech translation. To measure latency, I need to know which phrase in the source audio corresponds to which phrase in the translated audio, so I can measure the time gap between them. This alignment used to be done by hand. A person listens to both tracks, matches up the phrases, and logs timestamps. For a 6-minute session that's easily an afternoon of work.\nThe hard part isn't the math. It's the alignment. Languages reorder things. German puts verbs at the end. Arabic restructures sentences. A Spanish phrase at position 3 might map to an English phrase at position 7.\nThis is exactly the kind of thing LLMs are good at. They understand semantic equivalence across languages and handle reordering naturally. So I replaced the manual step with an LLM call:\nWhat used to take hours now takes a couple of minutes. No human in the loop.\nThe reason I'm sharing this is that the pattern generalizes. If you have a workflow step where a human reads two things and figures out how they correspond, an LLM can probably do it. The key is that I'm not asking it for a judgment call or creative output. I'm asking it to do structured alignment, a well-constrained task where it's reliable.\nThe LLM only handles the one step that actually needs language understanding. Everything else (force alignment, timestamp extraction, aggregation) is regular code.\nFull methodology: Automating ear-voice span\nCode: VoiceFrom/live-s2st-eval", "url": "https://wpnews.pro/news/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand", "canonical_source": "https://dev.to/yahya_saleh_d157cf3d7fe2e/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand-16c2", "published_at": "2026-05-23 15:15:00+00:00", "updated_at": "2026-05-23 15:32:40.960882+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "machine-learning", "developer-tools"], "entities": ["LLM"], "alternates": {"html": "https://wpnews.pro/news/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand", "markdown": "https://wpnews.pro/news/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand.md", "text": "https://wpnews.pro/news/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand.txt", "jsonld": "https://wpnews.pro/news/using-an-llm-to-automate-a-task-that-used-to-take-hours-by-hand.jsonld"}}