While search technology has advanced rapidly in recent years, most organizations still struggle with the longstanding problem of finding what they actually need. The issue isn't a lack of data. It's finding the right data when you need it. Traditional keyword search? Limited. Modern semantic search? Not perfect either. Turns out, they're both pretty flawed on their own.

Lexical search excels at precision. It matches exact keywords using algorithms like BM25, scoring documents based on how frequently terms appear. Simple, interpretable, and computationally efficient. But it's also painfully literal. No exact match? No results. End of story.

Precise but stubborn—lexical search finds exactly what you ask for, nothing more, nothing less.

Vector search takes a completely different approach. Using models like SentenceTransformers, it generates dense embeddings that capture the meaning behind words. Same concept, different wording? No problem. It understands intent and context beyond exact matches. Clever, but resource-intensive and less transparent than its lexical counterpart. With AI adoption rates growing rapidly as 77% of Americans use AI daily, the demand for better search solutions has never been higher.

Enter hybrid search – the superhero fusion no one asked for but everyone needs. It combines both approaches to create something better than either method alone. Research demonstrates that dense retrievers excel with strong relevance signals but often struggle with weaker ones. Elastic offers best-in-class retrieval performance through its hybrid search that combines learned sparse encoder with BM25. There are several ways to do this. Some systems use Reciprocal Rank Fusion to blend result rankings. Others apply a linear combination formula: H = (1-α)K + αV, where K represents lexical scores and V represents vector scores. The α parameter? That's just fancy talk for "how much semantic juice do you want in your search cocktail."

The benefits are obvious. Higher accuracy. Better recall. Reduced false negatives. Documents that would slip through the cracks of either method alone suddenly become findable. And it scales – even to petabyte-sized datasets on platforms like Elasticsearch and OpenSearch.

Sure, there are challenges. Balancing scores requires tuning. But the payoff is worth it. When one search fails, the other picks up the slack. Together, they're unstoppable. Like peanut butter and jelly, only for finding stuff in your massive data pile.