Comparison of dense vector retrieval (neural embeddings) and sparse retrieval (keyword-based) approaches including strengths, weaknesses, and when to use hybrid methods.
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What: Neural embeddings in continuous vector space
Strengths:
Weaknesses:
Best For:
What: Traditional keyword-based methods (TF-IDF, BM25)
Strengths:
Weaknesses:
Best For:
| Aspect | Dense | Sparse |
|---|---|---|
| Semantic | ✓✓ | ✗ |
| Exact Match | ✗ | ✓✓ |
| Speed | Medium | Fast |
| Interpretable | ✗ | ✓✓ |
| Setup Cost | High | Low |
| Storage | Medium | Low |
| Out-of-domain | Good | Poor |
Combination Methods:
Result Fusion:
Benefits:
Implementation:
# Get results from both
sparse_results = bm25_search(query)
dense_results = vector_search(embed(query))
# Combine with RRF
final = reciprocal_rank_fusion(
sparse_results,
dense_results,
k=60
)
SPLADE, etc.:
Dense Only:
Hybrid Native:
Use Dense When:
Use Sparse When:
Use Hybrid When:
Industry moving toward hybrid as default for production RAG systems, with ~70% of production systems using some form of hybrid retrieval.