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Cascaded vs Speech-to-Speech: How to choose the right voice agent architecture?
Cascaded vs Speech-to-Speech is the choice between building a voice agent as a chain of three models (speech-to-text, then an LLM, then text-to-speech) or as one model that hears audio and speaks audio directly. We choose based on what matters most for our use case: control and accuracy, or speed and natural sound.
How do you build a latency budget for a voice AI agent?
A latency budget for a voice AI agent is a plan that splits the total waiting time we allow for a reply into smaller pieces, one for each step of the agent, so that the agent always starts replying fast enough to feel natural.
What is a Neural Network and How Does It Work?
A Neural Network is a model made of many small units called neurons, arranged in layers, that turn input numbers into a prediction. It learns by adjusting its numbers again and again until its predictions become close to the correct answers.
What is barge-in in a voice AI agent?
Barge-in in a voice AI agent is the ability of the user to interrupt the agent while it is speaking, and the ability of the agent to stop talking right away and listen.
What is turn-taking in a voice AI agent?
Turn-taking in a voice AI agent is the skill of knowing when the user has finished speaking, when the agent should start speaking, and when the agent should stop because the user wants to talk again.
Why Do LLMs Hallucinate? The Token-Level Explanation
LLMs hallucinate because they write an answer one token, a small piece of text, at a time by picking a likely next token, and when they do not know the right token, they still have to pick one, so a confident-looking guess comes out.
How does ChatGPT remember you?
ChatGPT remembers you by saving small notes about you outside the AI model and adding those notes back into the conversation whenever you chat. The model itself does not change when you chat with it, it reads your notes before it replies.
How do Browser Agents work?
Browser Agents are AI agents that use a web browser like a human does. They look at a web page, decide what to click or type, do it, look at the new page, and repeat until the task is finished.
How do Query Rewriting and Multi-Query Retrieval work?
Query Rewriting and Multi-Query Retrieval are two ways of improving the search step of an AI system. Query Rewriting uses an LLM to turn the user's messy question into one clear search query, and Multi-Query Retrieval uses an LLM to turn the question into several different search queries, searches with all of them, and merges the results.