AI Quick Reference

Looking for fast answers or a quick refresher on AI-related topics? The AI Quick Reference has everything you need—straightforward explanations, practical solutions, and insights on the latest trends like LLMs, vector databases, RAG, and more to supercharge your AI projects!

• How can one determine if the embedding dimensionality is appropriate for the task, and what might be the impact of reducing dimensions (via techniques like PCA) on both performance and accuracy?
• In evaluating recall vs latency trade-offs, what is a good methodology to determine the optimal operating point for a system? (e.g., plotting a recall-vs-QPS curve and choosing a target recall)
• If a RAG system’s answers are poor, how can we determine whether the fault lies with retrieval or generation? (Hint: evaluate retrieval accuracy separately with metrics like recall@K.)
• How can one experiment to determine which distance metric yields the best retrieval quality for a given task (e.g., trying both cosine and Euclidean and comparing recall/precision of results)?
• When presenting benchmark results, what are effective ways to visualize and report the performance (throughput, latency, recall) to make it actionable for decision makers?
• What methods can be used to estimate the storage size of an index before building it (based on number of vectors, dimension, and chosen index type)?
• How can we incorporate metrics like nDCG (normalized discounted cumulative gain) to evaluate ranked retrieval outputs in a RAG context where document order may influence the generator?
• What are some standard benchmarks or datasets used to test retrieval performance in RAG systems (for instance, open-domain QA benchmarks like Natural Questions or WebQuestions)?
• How can one evaluate the retrieval performance of a vector database if the exact ground-truth nearest neighbors are not known for a dataset (for example, using human relevance judgments or approximate ground truth)?
• Beyond basic recall and precision, which other metrics (such as nDCG, MRR, or F1-score) can be used to evaluate vector search results, and what aspects of performance does each capture?
• How would you evaluate whether the retriever is returning the necessary relevant information for queries independently of the generator’s performance?
• What techniques can be used to handle heavy query loads on a vector database (e.g., batching multiple queries together, asynchronous querying, or load balancing across replicas)?
• What monitoring or profiling tools can help identify the stages of the vector query process that contribute most to latency (e.g., CPU profiling to see time spent computing distances vs waiting on I/O)?
• What techniques can be used to tune the system for better cache utilization (for example, controlling data layout or batch sizes) to improve performance?
• When integrating a vector search system into a larger pipeline (like RAG or a recommendation system), how do you ensure the vector DB is tuned in concert with the rest of the system (embedding model, etc.)?
• How do you measure the impact of different distance metrics on the performance of a vector database during testing? (For instance, testing the same queries under cosine similarity vs Euclidean distance.)
• How can one plan capacity for a vector database cluster when anticipating growth (e.g., provisioning for index size, query load, and maintaining performance headroom)?
• In a scenario where query throughput is more important than absolute recall, what configuration changes might you apply to the index or search parameters to prioritize speed?
• How can one reduce the dimensionality or size of embeddings (through methods like PCA or autoencoders) to make a large-scale problem more tractable without too much loss in accuracy?
• What techniques can be used to reduce the latency of vector searches? (Think of using faster hardware like GPUs, tuning index parameters for speed, or caching mechanisms.)
• In terms of service level agreements (SLAs), how would you set a latency target for a vector search, and what configuration or architecture decisions ensure meeting that target under load?
• What steps would you take to systematically tune a vector database for a specific application’s workload (consider tuning one parameter at a time, using grid search or automatic tuning methods)?
• How can the parameters of an IVF index (like the number of clusters nlist and the number of probes nprobe) be tuned to achieve a target recall at the fastest possible query speed?
• How would you approach tuning a vector database that needs to serve multiple query types or multiple data collections (ensuring one index’s configuration doesn’t negatively impact another’s performance)?
• In what ways do tree-based indices (such as Annoy’s random projection forests) differ from graph-based indices (like HNSW) in terms of search speed and recall?
• What is the impact of using disk-based ANN methods (where part of the index is on SSD/HDD) on query latency compared to fully in-memory indices?
• How do vector databases like Milvus or Weaviate handle storage of vectors and indexes under the hood (e.g., do they use memory-mapped files, proprietary storage engines, etc.)?
• How do vector indexes handle dynamic updates (inserts or deletes of vectors)? For instance, what are the challenges of updating an Annoy index compared to an HNSW index?
• How does vector quantization (e.g., Product Quantization) help reduce the storage requirements of vector indexes, and what is the impact on search accuracy when using quantized vectors?
• What are some distinctive features of Weaviate as a vector search engine, especially regarding its support for hybrid search, modules (like transformers), or GraphQL queries?
• If a vector database supports multiple distance metrics, how might the index be stored or optimized differently for each (for example, an index optimized for inner product vs one for L2)?
• What are common pitfalls or mistakes to avoid when benchmarking vector databases (such as not using enough queries, or not accounting for initialization overhead in timing)?
• In terms of index build time and update flexibility, how do different indexing structures (e.g., FLAT, IVF, HNSW, Annoy) compare with each other?
• When comparing two different vector databases or ANN algorithms, how should one interpret differences in their recall@K for a fixed K? (For instance, is a 5% recall improvement significant in practice?)
• What techniques are available for effectively searching over data that is split into multiple indexes due to size (like hierarchical routing of queries to the most relevant partition)?
• What hardware considerations (using more but cheaper nodes vs fewer powerful nodes, using NVMe SSDs, etc.) come into play when dealing with very large vector indexes?
• What trade-offs emerge when scaling: for example, is it more efficient to have one large index on a beefy node or to split into many smaller indexes on multiple smaller nodes?
• What happens to index build time and query performance as the number of vectors grows from 1 million to 1 billion? What scaling behaviors (linear, sublinear, etc.) are expected or observed?
• What adjustments need to be made to an ANN algorithm when switching from Euclidean to cosine similarity? (Consider that cosine similarity can be achieved via normalized vectors and Euclidean distance.)
• When testing large-scale performance, what proxies or smaller-scale tests can be done if one cannot afford to test on the full dataset size initially?
• In practical terms, what differences might you observe in a search system when using cosine similarity instead of Euclidean distance on the same set of normalized embeddings?
• In terms of distance metrics, which of these tools offer flexibility in choosing the metric (Euclidean vs Cosine vs others), and are there any limitations on metric choice per tool?
• When the dataset size exceeds available RAM, what approaches can be used to still perform vector search (e.g., disk-based indexes, streaming data from disk, or hierarchical indexing)?
• Are there known benchmarks or case studies of vector search at massive scale (hundreds of millions or billions of points), and what do they highlight about system design and best practices?
• Can using an inappropriate distance metric for a given embedding lead to poorer results (for example, using Euclidean on embeddings where only the direction matters)?
• How do inverted file (IVF) indexes work in vector databases, and what role do clustering centroids play in the search process?
• What trade-offs exist between using an exact brute-force search versus an approximate index in a vector database (considering factors like speed, memory, and accuracy)?
• When might it be acceptable to use brute-force (linear) search over vectors despite its O(n) query complexity (consider small datasets or high-accuracy requirements)?
• What is the typical time complexity of popular ANN (Approximate Nearest Neighbor) search algorithms, and how does this complexity translate to practical search speed as the dataset grows?
• Why do approximate search methods achieve significantly faster query times than brute-force search, and what is the usual trade-off involved in this speed-up?
• What are the performance implications of increasing the number of centroids (clusters) in an IVF index on search speed and recall?
• Why might an exact search be nearly as efficient as an approximate search for certain scenarios (such as very low-dimensional data or small datasets), and what does this imply about index choice?
• What is the relationship between search recall and throughput, and how can one adjust system settings to achieve the needed balance for a specific application?
• In practical benchmark reports, how are recall and QPS (queries per second) reported together to give a full picture of a vector database’s performance?
• How does a vector database handle scaling up to millions or billions of vectors, and what architectural features enable this scalability?
• How is data typically partitioned or sharded in a distributed vector database, and what challenges arise in searching across shards for nearest neighbors?
• What strategies allow continuous addition of new vectors in a scalable way (streaming data) without reindexing everything from scratch? (e.g., dynamic indexes or periodic rebuilds)
• How does memory consumption grow with dataset size for different index types, and what methods can be used to estimate or control memory usage when scaling up?
• How do systems like Milvus facilitate scaling in practice—what components do they provide for clustering, load balancing, or distributed index storage?
• How does increasing the number of probes or search depth (like nprobe or efSearch) impact query latency, and how can one find an optimal setting that balances speed and recall?
• How do advanced hardware options (like vector processors, GPU libraries, or FPGAs) specifically help in lowering the latency of high-dimensional similarity searches?
• How can you simulate a production-like environment when measuring latency (accounting for concurrent queries, network delays, etc.) to ensure the measurements are realistic?
• How does an IVF-PQ index differ from a plain IVF index in terms of storage footprint and accuracy trade-offs?
• When dealing with extremely large vector sets, what storage mediums are commonly used (RAM vs SSD vs HDD), and how do these choices affect search performance and index build times?
• How do FAISS and Annoy compare in terms of index build time and memory usage for large datasets, and what might drive the decision to use one over the other?
• What factors should be controlled to make fair performance comparisons between two vector database systems (e.g., ensuring the same hardware, similar index build configurations, and using the same dataset)?
• What is the significance of using standard benchmark datasets (like SIFT1M, GloVe, DEEP1B) in evaluating vector search, and what are the pros and cons of relying on those for decision making?
• How can one test the scalability limits of a vector database (for example, by progressively increasing dataset size or query concurrency until performance degrades)?
• How might one include the cost of operations (CPU, memory usage, or even monetary cost for cloud services) into the evaluation, rather than just raw speed and accuracy metrics?
• What strategies can be employed to ensure that search remains fast as data scales (such as using multiple levels of coarse-to-fine search, or using prefilters to narrow down candidates)?
• How do vector databases handle backup and restore or replication for very large datasets, and what impact does that have on system design (in terms of time and storage overhead)?
• What is the relationship between vector normalization and the choice of metric (i.e., when and why should vectors be normalized before indexing)?
• How do vector database services that don’t expose index parameters handle tuning under the hood, and what can a user do to indirectly influence performance (like choosing index type or instance size)?
• How can we measure the accuracy of the retrieval component in a RAG system (for example, using metrics like precision@K and recall@K on the documents retrieved)?
• What is an acceptable range of retriever recall for a RAG system aiming to answer questions correctly most of the time, and how might this vary by application domain?
• When comparing two different retrievers or vector search configurations for RAG, what retrieval evaluation criteria should we look at to determine which one is better?
• What are embeddings in vector search?
• What is vector search?
• How does a vector database handle multimodal data?
• How does a vector database support vector search?
• What is a vector in the context of vector search?
• What is the role of AI in optimizing vector search?
• What is approximate nearest-neighbor (ANN) search?
• How do I choose the right similarity metric (e.g., cosine, Euclidean)?
• How does clustering improve vector search?
• What is cosine similarity in vector search?
• How does dimensionality affect vector search performance?
• What is the role of embeddings in vector search?
• How do I evaluate vector search performance?
• What are the differences between exact and approximate vector search?
• What is the difference between exact and approximate vector search?
• How do I handle high-dimensional vectors in vector search?
• How does hardware (e.g., GPUs) affect vector search speed?
• How is indexing done in a vector database?
• How does indexing affect the speed of vector search?
• How do I integrate vector databases with existing systems?
• What are next-gen indexing methods for vector search?
• Can vector search replace traditional search entirely?
• How will quantum computing affect vector search?