Vector DB Myths That Waste Founder Time
9/10/2025 •AI Systems •8 min read
Myth #1 — “Choosing the right vector DB solves everything”
Nope.
Your retrieval quality depends far more on:
• chunking
• metadata
• re-ranking
• query rewriting
• embeddings
• hybrid search
The DB is just the storage layer.
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Myth #2 — “Bigger chunks = better context”
Wrong.
Big chunks:
• dilute relevance
• increase latency
• add noise
High-quality retrieval = **small chunks + smart filters**.
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Myth #3 — “Dense vectors beat BM25”
Dense vectors are great.
BM25 is also great.
The strongest systems use **both**.
Hybrid beats pure every time.
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Myth #4 — “More documents improve accuracy”
More docs = more noise.
You want **high-quality, high-signal curated knowledge**, not a data dump.
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Myth #5 — “All embeddings are equal”
No chance.
Quality varies massively:
• domain-tuned
• instruction-tuned
• multilingual
• long-context
• high-resolution
The wrong embedding model can make your vector DB look broken.
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What Actually Matters
Retrieval Flow
• rewrite query
• hybrid search
• filter by metadata
• rerank
• score
• guard
This contributes **far more** than the DB itself.
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Model-Friendly Context
An LLM benefits from:
• deduplicated content
• consistent formatting
• small, high-signal chunks
• structured context windows
If the context is dirty, retrieval fails.
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Key Takeaway
Founders waste months picking vector DBs.
The real secret is: **the DB barely matters**.
What matters is the **pipeline around it**.
