HighLevel Voice AI Course: Lesson 5: LLM & Components of Voice AI | The Brain Behind Voice AI Agent скачать в хорошем качестве

HighLevel Voice AI Course: Lesson 5: LLM & Components of Voice AI | The Brain Behind Voice AI Agent

I am on a mission to create the most comprehensive and updates HighLevel Voice AI Course. Here is the Lesson#5 in #gohighlevel #voiceai course. 📞 Want to build a fast, natural-sounding Voice AI agent? In this video, I break down the full Voice AI stack and explain where LLM models fit as the “brain” of your agent. You’ll learn the 3 core layers behind every Voice AI call: ✅ STT (Speech-to-Text) — transcribers like Deepgram/Whisper ✅ LLM (Brain + orchestration) — prompts, turn-taking, guardrails, functions ✅ TTS (Text-to-Speech) — voice providers like ElevenLabs, Cartesia, etc. Then we jump into a real setup view (Vapi/Vappy-style) to see how changing your model, transcriber, and voice provider impacts: ⚡ Latency (responsiveness) 💰 Cost per minute 🎧 Call quality + user experience If you’re choosing between models/providers, this will help you find the best “sweet spot” for your use case. Chapters / timestamps (YouTube-friendly) 00:00 What we’re covering: LLMs + Voice AI layers 00:24 Layer 1 — STT (Speech-to-Text) in Voice AI 01:12 Transcribers: Deepgram, Whisper + other options 01:49 Layer 2 — LLM as the “brain” (prompts + orchestration) 02:13 Functions inside the LLM: turn-taking, emotions, backchanneling 03:44 Layer 3 — TTS (Text-to-Speech) voice providers 04:48 Full Voice AI call loop (STT → LLM → TTS) 06:03 Demo setup view: selecting provider + model 06:42 Latency explained (why it matters for call experience) 07:13 Comparing model latency + cost (examples) 08:43 Voice provider options + impact on latency/cost 09:20 Transcriber selection + language settings 10:03 Transcriber latency comparison (example changes) 11:01 Total latency breakdown + improving responsiveness 11:48 Cost breakdown (hosting vs transcription vs model vs voice) 12:33 Reviewing available LLM models + picking the best fit 13:06 Testing models + guardrails + avoiding hallucinations 13:33 Recommended model choice + wrap-up