How to Build AI-Powered Search for Your Website
Replace basic keyword search with AI-powered semantic search that understands user intent and returns genuinely relevant results.
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What You'll Learn
This intermediate-level guide walks you through how to build ai-powered search for your website step by step. Estimated time: 14 min.
Step 1: Choose your search architecture
Decide between vector search (Pinecone, Weaviate), hybrid search with pgvector, or a full reranking pipeline.
Step 2: Index your content
Generate embeddings for your website content using OpenAI or Cohere embedding models and store in your vector database.
Step 3: Build the search API
Create a search endpoint that converts user queries to embeddings, retrieves similar content, and optionally reranks results.
Step 4: Add query understanding
Use an LLM to expand queries, handle typos, and understand user intent for better retrieval.
Step 5: Implement search analytics
Track search queries, click-through rates, and zero-result queries to continuously improve search quality.
Frequently Asked Questions
How much does AI search improve over keyword search?▾
Semantic search improves relevance by 30-50% for natural language queries by understanding meaning and intent rather than just matching words.
What is the simplest way to add AI search?▾
Add pgvector to your existing PostgreSQL database and embed your content. This avoids managing a separate vector database service.
How much does AI search cost to run?▾
Embedding generation costs $0.01-0.10 per million tokens. Vector search hosting ranges from free (pgvector) to $70+/mo for managed services like Pinecone.