Latency & Critical Path Flashcards
What is latency in a chatbot?
Latency is the delay between a user’s query and the chatbot’s response.
Why is high latency problematic for chatbots?
High latency leads to a poor user experience because users expect quick replies.
What is a major cause of latency in modern conversational AI?
A major cause is the complexity of the processing pipeline, where multiple models and steps are used to produce an answer.
What is meant by the ‘critical path’ in a chatbot’s processing pipeline?
The critical path is the shortest and most efficient sequence of dependent steps (or models) that must be executed in sequence to produce a response.
How can a slow step on the critical path affect the chatbot?
If any step on the critical path is slow, it slows down the entire response due to dependencies between models.
What is one strategy to reduce latency by addressing unnecessary processing steps?
Streamlining the pipeline involves removing or bypassing unnecessary processing steps so that only critical components are executed.
How can parallel processing help in reducing latency?
Parallel processing allows independent tasks to run simultaneously instead of sequentially, reducing overall response time.
In what ways can optimizing models and code help reduce latency?
Optimizing models and code involves using more efficient algorithms, optimized libraries, caching frequent results, and identifying bottlenecks to speed up processing.
How can upgrading infrastructure contribute to lower latency?
Upgrading infrastructure by using faster hardware, such as powerful servers, GPUs, or specialized accelerators, and employing load-balancing can reduce processing time.
How does efficient data handling play a role in reducing latency?
Efficient data handling, like using faster databases, in-memory caches, or pre-loading data, minimizes delays caused by external data fetches or lookups.
What trade-off must developers consider when increasing the complexity of the language model?
Developers must balance increased complexity (which can improve answer quality) with the need to keep latency low, finding a sweet spot between accuracy and responsiveness.
