The Retrieval Quality Problem

Embedding-based semantic search provides intuitive relevance matching, but production systems quickly reveal its limitations. Pure vector similarity misses exact keyword matches and struggles with domain-specific terminology. Hybrid retrieval architectures combine dense embeddings with sparse lexical methods like BM25 (Best Matching 25), using reciprocal rank fusion to merge results. Metadata filtering adds another layer, enabling attribute-based constraints that vector similarity alone cannot capture. This multi-stage approach consistently outperforms single-method retrieval in real-world deployments.

Retrieval Performance Comparison

Artifact: Table

Reranking models like cross-encoders evaluate query-document pairs directly, providing accuracy gains of 10-15% over initial retrieval. The cost is latency, making reranking ideal for top-k refinement rather than full corpus search.

Context Engineering Over Model Scale

Context window organization affects output quality more than model size. A well-structured prompt with 4k tokens in GPT-3.5 often outperforms poorly organized 32k context in GPT-4. Position matters—critical information placed at the beginning or end of context windows shows better retention than middle-positioned content. Chunking strategies directly impact this. Naive character-based splitting breaks paragraphs mid-sentence, destroying semantic coherence. Domain-aware approaches respect document structure: splitting on section headers for documentation, paragraph boundaries for articles, or logical blocks for code. Recursive chunking with overlap maintains continuity across boundaries.

Vector Database Selection Criteria

Approximate nearest neighbor algorithms power vector databases, with each offering different accuracy-speed profiles. HNSW (Hierarchical Navigable Small World) provides high recall with graph-based indexing but consumes significant memory. IVF (Inverted File) methods partition vector space for faster search at the cost of reduced accuracy. Product quantization compresses vectors, trading precision for storage efficiency. The choice depends on scale and latency requirements. Sub-100ms retrieval with 95%+ recall works for most applications, but real-time systems may accept 85% recall for 20ms latency. Benchmarking against your actual query distribution and dataset size reveals the right balance.