A virtual assistant can feel helpful for a few users yet become slow, expensive, or inconsistent as requests and stored information grow. Scalability keeps acceptable responsiveness and quality while supporting a large, diverse user base. section 9.3.2 section 6.5.1
The practical goal is to spend effort where it matters: answer straightforward questions quickly, while reserving deeper processing for complex, multi-step requests. Contextual awareness and user history can improve relevance without requiring the same level of personalization for every interaction. section 9.3.2
Evaluate scalability through:
- Indexing speed and storage requirements, which affect how efficiently a knowledge base grows. section 6.5.1
- Query responsiveness, including latency and queries per second, while recognizing that speed alone does not measure retrieval quality. section 6.5.1
- Recall rate, because missing relevant information can make a fast retrieval system produce inadequate answers. section 6.5.1
Context without unnecessary cost
A travel assistant can adapt recommendations to whether someone is at an airport or browsing from home. It can also prioritize vegan recipes for a user who frequently asks about them, while keeping simple interactions concise and reserving sophisticated processing for complex requests. section 9.3.2
Table 6.3 Critical metrics that influence the efficiency and accuracy of a RAG system’s document index
|
Aspect
|
Summary
|
Example
|
|---|---|---|
|
Indexing metrics
|
Evaluates speed, storage needs, and scalability. Critical for large-scale data systems.
|
Indexing for a news aggregator where speed and scale are crucial
|
|
Vector database performance
|
Measures performance in handling high-dimensional data
|
For technical support, accurate troubleshooting steps must be assembled for multiple documents. E.g., “Why is my device overheating?”
|
|
Recall rate
|
Indicates accuracy in retrieving relevant data. High recall is vital for complete retrieval.
|
In legal document retrieval, high recall ensures all relevant cases are found.
|
|
Query complexity
|
Affects performance based on query specifics, dimensionality, and dataset diversity
|
Financial databases handling complex queries across multiple data points
|
|
Benchmarking tools
|
Tools like ANN-Benchmark compare algorithms on metrics like recall versus QPS.
|
Evaluating which algorithm best balances speed and accuracy for a video search engine
|
Compare indexing speed, storage needs, vector database performance, recall rate, query complexity, and benchmarking considerations when evaluating a RAG system. section 6.5.1
