Why Most Search Engines Fail: What You’re Missing About Vector, BM25, and Hybrid Search
Everything Product Teams Need to Know About Modern AI Search: BM25 vs Vector vs Hybrid
Who Should Read This?
This guide is for product owners, technical teams, and decision-makers who need users to quickly find information—whether in customer support, e-commerce, or internal tools—and want practical answers on choosing the right AI search method.
Why So Many AI Search Engines Miss the Mark
It’s easy to buy into the hype: “AI search is smart—it just works.” But reality is different.
Many users still complain about irrelevant results, missed answers, and slow search. The problem? Most teams choose the wrong search method for their specific data and user behavior.
Here’s what you’re likely missing—and how to fix it.
Three Search Approaches You Need to Know
1. Vector Search: Finds Meaning, Not Just Words
What it is: Uses AI to understand the intent and context of a query, not just keywords.
When to use: Great for natural language queries, customer support, or knowledge bases—anywhere users don’t know the exact wording.
Example:
User types: “How do I get my money back for a damaged order?”
Vector search finds: “Refunds and returns for faulty products,” even if it doesn’t contain the exact phrase.
Pros: Accurately matches user intent even with synonyms or typos in unstructured queries.
Cons: Sometimes retrieves off-topic results and demands higher processing power.
2. BM25: Finds Exact Matches, Fast
What it is: Classic keyword search that ranks results by exact word matches.
When to use: Ideal for structured data (catalogs, SKUs, technical docs) with precise, predictable queries.
Example:
User types: “GST invoice download”
BM25 surfaces documents that specifically contain “GST,” “invoice,” and “download.”
Pros: Delivers fast, precise results and is easy to audit.
Cons: Misses relevant matches if users use synonyms or less common phrasing.
3. Hybrid (Ensemble): The Best of Both Worlds—If You Do It Right
What it is: Combines vector and BM25 to provide both exact keyword matches and semantically similar results.
When to use: Best for platforms with diverse content and user search styles, like e-commerce with products, reviews, and support articles.
Example:
Query: “Wireless headphones with long battery”
BM25 returns: “Wireless headphones, 20-hour battery life”
Vector returns: “Top Bluetooth headphones for travel and long listening.”
In short:
Hybrid search surfaces both direct hits and smart alternatives, helping users find what they mean—even if they don’t type it exactly.
Pros: Balances accuracy and flexibility, giving users the broadest relevant results.
Cons: More complex to build and maintain, and needs regular tuning to prevent irrelevant or duplicate results.
How to Choose the Right Search Method
1. Identify User Search Patterns:
If most users search using specific codes, product names, or technical terms, BM25 is likely the best fit.
If users often type questions in their own words or use varied phrasing, consider Vector search.
If your users do both—or you serve multiple types of users—Hybrid will give the most complete results.
2. Assess Your Data Structure:
For well-organized, structured content (like catalogs, databases, or documentation), start with BM25.
For unstructured or mixed content (like FAQs, emails, chat logs, or reviews), go with Vector or Hybrid.
3. Pilot and Compare:
Run your actual user queries through BM25, Vector, and Hybrid.
Measure which approach helps users find the right answers fastest and with fewer attempts.
4. Plan for Upkeep:
BM25 is straightforward to manage and requires minimal ongoing work.
Vector needs occasional model updates and may require more hardware.
Hybrid provides the most coverage but requires ongoing monitoring and adjustment to stay effective.
BM25 is fast for exact searches, Vector understands natural language but can be broad, and Hybrid covers both needs but is more complex to maintain.
Scenario: Finding the Perfect Headphones
You run search for an online electronics store.
A customer searches for:
“Wireless earbuds with long battery life and noise cancellation”
products = [
"ANC TWS, 40-hour playback, quick charge", # Jargon only
"Noiseless pods, marathon battery, splashproof", # Synonyms only
"Wireless earphones, 15 hours playback, noise reduction, sport fit", # Partial match
"Classic headphones, 8-hour runtime, retro look", # No relevant keywords
"Earbuds with active sound muting, 2-day power, case included", # Only semantic similarity
"Bluetooth headphones, bass boost, long lasting", # Not relevant
"Studio cans, passive noise control, cable", # Not relevant
"Earphones for gym, fast charge, sweatproof", # Not relevant
]
query = "Wireless earbuds with long battery life and noise cancellation"
BM25 (Keyword Search)
Non-Technical:
BM25 looks for products that include the same keywords from the customer’s search. The more words that match, the higher the product appears in results. It won’t understand abbreviations or synonyms unless they’re present in the data.
Technical:
from rank_bm25 import BM25Okapi
tokenized_products = [p.lower().split() for p in products]
bm25 = BM25Okapi(tokenized_products)
tokenized_query = query.lower().split()
bm25_scores = bm25.get_scores(tokenized_query)
bm25_ranked = [products[i] for i in sorted(range(len(products)), key=lambda x: bm25_scores[x], reverse=True)]
print("\nBM25 Results (top 5):")
for item in bm25_ranked[:5]:
print("-", item)
BM25 Results (top 3):
- Wireless earphones, 15 hours playback, noise reduction, sport fit
- Classic headphones, 8-hour runtime, retro look
- Bluetooth headphones, bass boost, long lastingNote: BM25 favors exact word matches (“earbuds,” “battery,” “wireless”) and may miss TWS/ANC/24H unless those terms are in the query.
Vector (Semantic Search)
Non-Technical:
Vector search uses AI to “understand” what the customer wants. Even if the product description uses different words (like “ANC” for “noise cancellation” or “20-hour playback” for “long battery life”), it still surfaces relevant products.
Technical:
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
product_embeddings = model.encode(products)
query_embedding = model.encode([query])[0]
cosine_scores = util.cos_sim(query_embedding, product_embeddings)[0]
vector_results = [products[i] for i in cosine_scores.argsort(descending=True)]
print("\nVector Results (top 3):")
for item in vector_results[:3]:
print("-", item)
# Output
Vector Results (top 3):
- ANC TWS, 40-hour playback, quick charge
- Noiseless pods, marathon battery, splashproof
- Earbuds with active sound muting, 2-day power, case includedHybrid (Combined Search)
Non-Technical:
Hybrid search runs both methods, then combines and ranks the results. This way, customers see both exact matches and smart alternatives—even if they use different words than your product listings.
Technical:
# Hybrid Search
hybrid_results = []
seen = set()
for item in bm25_ranked + vector_ranked:
if item not in seen:
hybrid_results.append(item)
seen.add(item)
print("\nHybrid Results (top 5):")
for item in hybrid_results[:5]:
print("-", item)
# Output
Hybrid Results (top 3):
- Wireless earphones, 15 hours playback, noise reduction, sport fit
- ANC TWS, 40-hour playback, quick charge
- Noiseless pods, marathon battery, splashproofConclusion
Choosing the right search approach isn’t just about technology—it directly shapes how easily people can find what matters, whether that’s customers shopping on your website or team members searching internal product docs, support tickets, or wikis.
BM25 is great for fast, exact matches but misses synonyms and evolving language.
Vector search understands intent, jargon, and user-friendly phrasing—helping everyone find the right answer, even if they don’t use official terms.
Hybrid search guarantees you capture both—the technical precision of keywords and the intelligence of semantic matching.
If you want people to move fast, make fewer mistakes, and actually use your knowledge and product data, invest in the right search method for your real-world content. The payoff is more efficient teams, happier customers, and a business that can scale.
Don’t rely on “good enough” search. Test these methods with your own data—internal or external—and you’ll see the difference where it counts.