Nathaniel Okenwa
Performant Architecture for a Fast Gen AI User Experience
#1about 2 minutes
Building a real-time translator inspired by sci-fi
The Babel fish from "Hitchhiker's Guide to the Galaxy" serves as the inspiration for a real-time audio translation project.
#2about 4 minutes
Analyzing the latency of a basic AI architecture
A demonstration of the initial 2019 architecture using GCloud reveals a significant latency of over ten seconds for a simple translation.
#3about 2 minutes
Reducing latency by upgrading the AI service stack
Switching to modern, specialized APIs like Deepgram and 11 Labs significantly cuts the total processing time from twelve to five seconds.
#4about 2 minutes
Implementing streaming to reduce response wait times
Adopting a streaming approach provides a major performance boost, but a naive implementation results in chaotic and low-quality audio output.
#5about 2 minutes
Using chunking to balance streaming speed and quality
Chunking data based on sentence punctuation controls the streaming waterfall, improving the quality of generated audio without sacrificing speed.
#6about 6 minutes
Eliminating network latency with local and edge models
Running a smaller, local AI model like Whisper on the edge eliminates cross-continental network latency and provides near-instantaneous results.
#7about 3 minutes
Using caching to serve pre-generated AI responses
Implementing caching, from simple request matching to semantic search with vector databases, avoids redundant generation and speeds up common queries.
#8about 2 minutes
Optimizing prompts and user experience for speed
Fine-tuning performance involves optimizing prompts to generate fewer tokens and improving perceived speed with clear loading states for the user.
#9about 2 minutes
Summary of key performance optimization techniques
A final recap covers the essential strategies for building fast Gen AI experiences, including streaming, edge computing, caching, and prompt optimization.
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