Performance Metrics
To evaluate how well our ChatGPT clone meets the requirements, we need to establish some key performance metrics:
Latency - This refers to the response time for queries. We'll want to minimize the time from when a user submits a text prompt to receiving the bot's response. Target could be <100ms for a natural feel.
Throughput - Measures how many user queries can be processed per second. To support millions of active users, the system needs high throughput in the range of hundreds or thousands of queries per second.
Availability - Percentage of time the system is operational and serving requests. We need to maximize uptime, with a target such as 99.95%.
Scalability - The ease of handling increased usage load by scaling out compute resources. Auto-scaling capabilities are necessary to support spikes in users.
Accuracy - Percentage of bot responses that are correct, relevant and coherent. Critical for usability, so we need to optimize conversation models for high precision.
By optimizing for low latency, high throughput and availability, scalability, and most importantly a high-degree of response accuracy, we can deliver on the core requirements.
These metrics guide the technical implementation decisions for components like infrastructure, machine learning, and data pipelines.
