Uploaded May 2026 | Updated September 2026, 2 weeks ago
At Granola’s London office, we hosted a live panel on how voice AI is moving beyond transcription into structured outputs, insights, and action.
Joined by Jonathan from Granola, Adrien from CoLoop, Shane from EdgeTier, and moderated by Ryan from AssemblyAI, we dug into the full voice AI pipeline: transcription quality, diarization, post-call vs. real-time tradeoffs, multilingual support, noisy audio, evaluation, and what it takes to turn raw conversations into useful product experiences.
The conversation covered what actually matters when building with voice today—and what still needs to get better.
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: assemblyai.com
🐦 Twitter: twitter.com/AssemblyAI
🦾 Discord: discord.gg/Cd8MyVJAXd
▶️ Subscribe: youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning #VoiceAI #VoiceAgents #AIAgents #LLM #SpeechToText #AssemblyAI #ConversationalAI
At Granola’s London office, we hosted a live panel on how voice AI is moving beyond transcription into structured outputs, insights, and action.
Joined by Jonathan from Granola, Adrien from CoLoop, Shane from EdgeTier, and moderated by Ryan from AssemblyAI, we dug into the full voice AI pipeline: transcription quality, diarization, post-call vs. real-time tradeoffs, multilingual support, noisy audio, evaluation, and what it takes to turn raw conversations into useful product experiences.
The conversation covered what actually matters when building with voice today—and what still needs to get better.
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: assemblyai.com
🐦 Twitter: twitter.com/AssemblyAI
🦾 Discord: discord.gg/Cd8MyVJAXd
▶️ Subscribe: youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning #VoiceAI #VoiceAgents #AIAgents #LLM #SpeechToText #AssemblyAI #ConversationalAI






![The Fundamentals of LLM Text Generation
Lets explore how Large Language Models (LLMs) like ChatGPT, Claude, Gemini generate text, focusing on decoding strategies that introduce randomness to produce human-like responses. We break down key sampling algorithms such as top-k sampling, top-p sampling (nucleus sampling), and temperature sampling. Additionally, we dive into an alternative method for text generation, typical sampling, based on information theory.
References:
[1] Locally Typical Sampling, by Clara Meister et al: https://arxiv.org/pdf/2202.00666
Video sections:
00:00 How LLMs generate text (Overview)
00:56 Why Randomness in text generation?
02:12 Top-k
03:22 Top-p
04:44 Temperature
06:04 Entropy and Information Content
07:12 Typical Sampling
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: https://www.assemblyai.com
🐦 Twitter: https://twitter.com/AssemblyAI
👽 Reddit: https://reddit.com/r/assemblyai
▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 Were hiring! Check our open roles: https://www.assemblyai.com/careers
🔑 Get your AssemblyAI API key here: https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_marco_2
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning The Fundamentals of LLM Text Generation](https://i.ytimg.com/vi/a-6hVvU1WMk/mqdefault.jpg)



