Semantic search systems balance accuracy (how relevant results are) and performance (how fast they return results). Higher accuracy often requires deeper analysis of text meaning, which can slow down queries. For example, using large language models like BERT to encode text into vectors captures nuanced relationships between words but requires significant computation. On the other hand, simpler methods like keyword matching or TF-IDF are faster but may miss context, leading to less precise results. Developers must choose between investing in complex models for better relevance or optimizing for speed with simpler approaches, depending on their use case.
One key tradeoff involves the choice of algorithms and infrastructure. For instance, exact nearest neighbor search in vector databases guarantees accurate matches by comparing every possible vector, but this becomes impractical with large datasets. Approximate Nearest Neighbor (ANN) algorithms like HNSW or FAISS speed up searches by accepting minor inaccuracies, reducing latency from seconds to milliseconds. However, this can cause top results to occasionally miss the most relevant matches. Similarly, preprocessing steps like indexing or caching improve performance but may limit flexibility—precomputed embeddings can’t easily adapt to new data without re-indexing, which impacts freshness and accuracy. These choices force developers to prioritize either real-time responsiveness or up-to-date, precise results.
Use cases also dictate where to strike the balance. In e-commerce, a fast but slightly less accurate search might be acceptable if it helps users quickly filter thousands of products. Missing a few relevant items matters less than keeping the interface responsive. In contrast, legal or medical search tools require high accuracy even if queries take longer, as overlooking critical information has serious consequences. Hybrid approaches, like combining keyword filters with semantic re-ranking, offer a middle ground: initial results are fetched quickly using simple methods, then refined with a smaller, more accurate model. For example, a system might use Elasticsearch for fast keyword-based retrieval and then apply a lightweight neural model to reorder the top 100 results. This balances speed and precision but adds complexity, requiring careful tuning to avoid overloading infrastructure.