The Promise of Vector Search
Vector search transformed how we find information. By converting text into high-dimensional vectors (embeddings), we can find semantically similar documents even when they don't share exact keywords. This was a massive leap forward from traditional keyword search.
But here's what most vendors won't tell you: vector search alone fails on 30-40% of real enterprise queries.
Where Vector Search Falls Short
1. Specificity Problems
Vector search finds similar content, not specific answers. Ask "What is our refund policy for orders over $500?" and vector search might return documents about refund policies, documents about orders over $500, and documents about customer service — but miss the one paragraph that addresses this exact intersection.
This is because embeddings capture semantic meaning at a broad level. Two documents can be "close" in embedding space without actually answering the same question.
2. Negation Blindness
Vector embeddings struggle with negation. "Documents that do NOT require manager approval" and "Documents that require manager approval" produce nearly identical embeddings. For compliance and legal teams, this distinction is critical.
3. Entity-Heavy Queries
When users ask about specific entities — customer names, product SKUs, contract numbers — vector search often fails because these identifiers don't carry semantic meaning. The embedding for "SKU-12345" tells the model nothing about what that SKU represents.
4. Multi-Hop Reasoning
"Who approved the budget for the project that was delayed in Q3?" requires following a chain: delayed project → its budget → the approver. Vector search can only find documents similar to the overall question, not traverse these logical steps.
The Multi-Strategy Solution
This is why Courdx uses 6+ retrieval strategies working together:
- Vector similarity for semantic understanding
- BM25 keyword search for exact matches and entity lookup
- Knowledge graph traversal for relationship-based queries
- HyDE (Hypothetical Document Embeddings) for bridging the query-document gap
- Semantic reranking to verify relevance after initial retrieval
- Corrective RAG to detect and fix low-confidence results
Each strategy covers the blind spots of the others. Vector search handles the "meaning" questions. BM25 handles exact entity lookups. The knowledge graph handles relationship queries that neither can answer.
Real-World Impact
In our benchmarks on enterprise document sets:
- Vector-only retrieval: 68% accuracy
- Vector + BM25: 78% accuracy
- Vector + BM25 + Knowledge Graph + Reranking: up to 95% accuracy in internal evaluations
The difference between 68% and 95% isn't academic — it's the difference between an AI system your team trusts and one they abandon after a week.
What This Means for Your Organization
If you're evaluating RAG platforms, ask these questions:
- What retrieval strategies does it use beyond vector search?
- How does it handle entity-specific queries?
- Can it traverse relationships between documents?
- Does it verify and self-correct its own results?
Any vendor that only offers vector search is leaving 30-40% of your team's questions unanswered.
Courdx combines 6+ retrieval strategies with automatic knowledge graph construction and self-correcting pipelines. Schedule a demo to see the difference.