Search Finds Nothing: LLM Exact-Match Disaster

The problem

An online marketplace's search function returned zero results for 92% of queries. Users searching for "mens shoes" found nothing, despite having 3,400 men's shoe listings. Support tickets complained: "Search is broken", "Can't find anything", "Is this site empty?". Analytics showed users performed an average of 4.7 failed searches before giving up. The company was losing $85,000/week in missed sales.

How AI created this issue

The developer asked Claude to implement a product search feature. Claude created a basic exact-match SQL query:

# Claude's search implementation
def search_products(query):
    # Simple exact match search
    sql = """
    SELECT * FROM products 
    WHERE LOWER(name) = LOWER(%s)
    OR LOWER(description) = LOWER(%s)
    ORDER BY created_at DESC
    """
    
    results = db.execute(sql, [query, query])
    return results

# This returns nothing for "mens shoes" because products are named:
# - "Men's Running Shoes - Nike Air Max"
# - "Casual Leather Shoes for Men"
# - "Mens Athletic Footwear"

Claude's search required exact matches of the entire field. It didn't handle plurals, typos, word order, or partial matches. The AI never suggested full-text search, fuzzy matching, or relevance scoring - just a basic string comparison that worked in the trivial example but failed with real user queries.

The solution

1. PostgreSQL full-text search implementation: Replaced exact match with intelligent search:

   
   # Proper search with PostgreSQL full-text search
   def search_products(query):
       # Create search vector from multiple fields
       sql = """
       WITH search_query AS (
           SELECT plainto_tsquery('english', %s) AS query
       )
       SELECT 
           p.*,
           ts_rank(search_vector, query) AS rank,
           ts_headline('english', name, query, 
               'StartSel=, StopSel=') AS highlighted_name
       FROM products p, search_query
       WHERE search_vector @@ query
       OR name ILIKE '%%' || %s || '%%'  -- Fallback partial match
       ORDER BY 
           CASE WHEN name ILIKE %s || '%%' THEN 0 ELSE 1 END,  -- Prioritize prefix matches
           rank DESC,
           popularity DESC
       LIMIT 50
       """
       
       # Handle common variations
       normalized_query = query.replace("mens", "men's").replace("shoes", "shoe")
       
       return db.execute(sql, [normalized_query, query, query])

2. Search index optimization: Added GIN indexes and search vectors:

   
   -- Add search vector column
   ALTER TABLE products ADD COLUMN search_vector tsvector;
   
   -- Populate with weighted fields
   UPDATE products SET search_vector = 
       setweight(to_tsvector('english', coalesce(name, '')), 'A') ||
       setweight(to_tsvector('english', coalesce(brand, '')), 'B') ||
       setweight(to_tsvector('english', coalesce(category, '')), 'C') ||
       setweight(to_tsvector('english', coalesce(description, '')), 'D');
   
   -- Create GIN index for fast searches
   CREATE INDEX idx_products_search ON products USING GIN (search_vector);
   
   -- Auto-update trigger
   CREATE TRIGGER update_search_vector 
   BEFORE INSERT OR UPDATE ON products
   FOR EACH ROW EXECUTE FUNCTION 
   tsvector_update_trigger(search_vector, 'pg_catalog.english', 
       name, brand, category, description);

3. Typo tolerance: Implemented trigram similarity for fuzzy matching
4. Synonym handling: Added common product synonyms (shoe/footwear, men's/mens)
5. Search analytics: Track failed searches to continuously improve

The results

• Search success rate jumped from 8% to 94%
• Average searches per session: 4.7 → 1.3
• Conversion rate increased 287% as users found products
• Support tickets about search dropped 96%
• Revenue increased $340,000/month from improved discovery
• Search response time: 1.2s → 85ms with proper indexing

The team learned that AI tools often implement the simplest possible solution that technically works. Real-world search requires understanding of linguistics, user behavior, and database capabilities. They now start with proven search solutions (Elasticsearch, PostgreSQL FTS) rather than asking AI to reinvent search from scratch.