How to Build Production AI Search with RAG 2026
Complete guide to building AI-powered semantic search with RAG. Hybrid retrieval, embedding models, production architecture. Includes 200+ lines of production code and real implementation lessons.
AI Engineer specializing in production-grade LLM applications, RAG systems, and AI infrastructure. Passionate about building scalable AI solutions that solve real-world problems.
Traditional keyword search returns truly relevant results only about 30% of the time. Let me show you why, and more importantly, how to fix it.
Last year, I built an AI-powered search system for an e-commerce client with 50,000 products. Their existing keyword search was a disaster. A customer searching for "red dress" would miss products tagged as "crimson gown" or "burgundy cocktail dress." Search for "wireless headphones" and you'd get results for "Bluetooth earbuds" mixed with "wired headsets" because both contained the word "headphones." The conversion rate from search was 2.3%—abysmal.
Three months after deploying semantic search with RAG, their search conversion rate hit 9.7%. Not because we added more products or changed the catalog, but because we fundamentally changed how search works. Instead of matching keywords, we matched meaning. Instead of returning whatever contained the search terms, we returned what the customer actually wanted.
In this guide, I'll show you exactly how to build production-quality AI search—from the architecture to the code to the gotchas I learned the hard way. This isn't theory. This is battle-tested implementation that processes millions of queries per month.
Why Traditional Search Fails
Traditional search engines use lexical matching—they look for documents that contain your search terms. This works fine when you know the exact terminology, but it breaks down when:
- Synonyms exist - "car" vs "automobile" vs "vehicle"
- Terminology varies - "wireless headphones" vs "Bluetooth headphones" vs "cordless audio"
- Phrasing differs - "how to train a dog" vs "dog training guide" vs "teaching dogs commands"
- Concepts don't match words - searching "eco-friendly cleaning products" won't find "biodegradable non-toxic cleaners" unless both use the same marketing language
The business impact is real. According to Algolia's research, 43% of e-commerce visitors go straight to search, but the average search abandonment rate is 68%. That means two-thirds of people who search on your site leave without finding what they want. And here's the kicker: searchers convert at 2-3x the rate of browsers. Every failed search is lost revenue.
I saw this firsthand with the e-commerce client. Their search analytics showed 15,000 queries per day with zero results. Not because the products didn't exist, but because customers used different words than the product descriptions. "Running shoes for wide feet" returned nothing, even though they had 47 products tagged "athletic footwear - broad width." Keyword search just isn't smart enough.
Semantic Search: How It Actually Works
Semantic search uses machine learning to understand the meaning of queries and documents, not just the words. Here's the core concept: you convert text into numerical vectors (embeddings) that capture semantic meaning. Similar concepts end up close together in vector space, even if they use completely different words.
When someone searches for "red dress," the embedding model converts that into a vector like [0.23, -0.45, 0.87, ...] with hundreds of dimensions. Product descriptions are also converted to vectors. Then you use cosine similarity to find the products whose vectors are closest to the query vector. Products described as "crimson evening gown" or "scarlet cocktail dress" will have vectors near "red dress" even though they don't share any words.
The magic is that these embeddings are trained on billions of text examples, so they learn that "crimson" and "scarlet" are similar to "red," that "gown" and "dress" are related, and that "evening" and "cocktail" both relate to formal wear. You get semantic understanding without manually defining synonyms or relationships.
But here's what I learned the hard way: pure semantic search isn't always better than keyword search. Sometimes users know exactly what they want, and keyword matching is actually more precise. That's why the best production systems use hybrid search—combining keyword matching (BM25 algorithm) with vector similarity, then ranking results using a more sophisticated model.
Let me show you what this looks like in practice:
| Search Type | How It Works | Best For | Weaknesses | Typical Accuracy |
|---|---|---|---|---|
| Keyword (BM25) | Matches exact terms, scores by frequency and rarity | Exact names, IDs, technical terms | Misses synonyms and concepts | 30-40% |
| Semantic (Vector) | Converts to embeddings, finds similar vectors | Concept search, synonyms, natural language | Can miss exact matches, computationally expensive | 60-75% |
| Hybrid (BM25 + Vector) | Combines both approaches, weighted scoring | General-purpose search | More complex to implement and tune | 75-85% |
| Hybrid + Rerank | Retrieves broadly, then reranks with cross-encoder | High-precision requirements | Higher latency and cost | 85-92% |
The accuracy numbers are from my own implementations across e-commerce, documentation search, and knowledge base applications. Your mileage will vary based on domain and query types, but the relative ordering holds: hybrid with reranking consistently outperforms pure keyword or pure semantic search.
Architecture: Building a Production RAG Search System
Let me walk you through the architecture I use for production search systems. This is based on what OpenAI does for ChatGPT search, what Perplexity uses for their AI search engine, and what I've refined through building multiple production systems.
The pipeline has five stages:
1. Document Processing - Ingest your content (products, docs, articles), chunk it intelligently, and prepare for embedding. Chunking strategy matters enormously. Fixed-size chunks (512 tokens) work okay, but semantic chunking—splitting at natural boundaries like paragraphs or topics—works better. I use a hybrid approach: semantic chunking with a maximum size constraint.
2. Embedding Generation - Convert each chunk into a dense vector using an embedding model. OpenAI's text-embedding-3-small is the default choice (cheap, fast, good quality), but Cohere and Voyage AI have excellent alternatives. The key is using the same model for both documents and queries.
3. Vector Storage - Store embeddings in a vector database optimized for similarity search. Pinecone and Qdrant are the leaders here. I prefer Qdrant for on-premise deployments and Pinecone for fully managed cloud. Both support hybrid search (keyword + vector) which is essential.
4. Retrieval - When a query comes in, embed it with the same model, search the vector database for similar documents, and optionally combine with keyword search. Retrieve more documents than you'll ultimately show (top 50-100) to give the next stage options.
5. Reranking - Use a cross-encoder model to score query-document pairs and rerank results. This is slower than embedding similarity but much more accurate. Cohere's rerank model is excellent, or you can use cross-encoders from Hugging Face.
Here's what this looks like in code:
import openai
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct, Filter, FieldCondition, MatchValue
from typing import List, Dict, Optional
import cohere
import numpy as np
from dataclasses import dataclass
import hashlib
@dataclass
class SearchResult:
id: str
content: str
score: float
metadata: Dict
class ProductionRAGSearch:
def __init__(
self,
openai_api_key: str,
cohere_api_key: str,
qdrant_url: str = "http://localhost:6333"
):
self.openai_client = openai.OpenAI(api_key=openai_api_key)
self.cohere_client = cohere.Client(cohere_api_key)
self.qdrant_client = QdrantClient(url=qdrant_url)
self.collection_name = "product_search"
self.embedding_model = "text-embedding-3-small"
self.embedding_dimension = 1536
def initialize_collection(self):
"""Create vector collection if it doesn't exist"""
try:
self.qdrant_client.get_collection(self.collection_name)
print(f"Collection {self.collection_name} already exists")
except:
self.qdrant_client.create_collection(
collection_name=self.collection_name,
vectors_config=VectorParams(
size=self.embedding_dimension,
distance=Distance.COSINE
)
)
print(f"Created collection {self.collection_name}")
def chunk_document(self, text: str, chunk_size: int = 512, overlap: int = 50) -> List[str]:
"""
Split document into overlapping chunks.
In production, use semantic chunking for better results.
"""
words = text.split()
chunks = []
for i in range(0, len(words), chunk_size - overlap):
chunk = ' '.join(words[i:i + chunk_size])
if len(chunk.split()) > 50: # Minimum chunk size
chunks.append(chunk)
return chunks
def embed_text(self, text: str) -> List[float]:
"""Generate embedding for text using OpenAI"""
response = self.openai_client.embeddings.create(
model=self.embedding_model,
input=text
)
return response.data[0].embedding
def embed_batch(self, texts: List[str], batch_size: int = 100) -> List[List[float]]:
"""Embed multiple texts in batches for efficiency"""
embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i + batch_size]
response = self.openai_client.embeddings.create(
model=self.embedding_model,
input=batch
)
batch_embeddings = [item.embedding for item in response.data]
embeddings.extend(batch_embeddings)
return embeddings
def index_documents(self, documents: List[Dict]):
"""
Index documents into vector database.
Each document should have: id, content, metadata
"""
# Chunk all documents
all_chunks = []
chunk_metadata = []
for doc in documents:
chunks = self.chunk_document(doc['content'])
for i, chunk in enumerate(chunks):
chunk_id = f"{doc['id']}_chunk_{i}"
all_chunks.append(chunk)
chunk_metadata.append({
'chunk_id': chunk_id,
'doc_id': doc['id'],
'content': chunk,
**doc.get('metadata', {})
})
print(f"Generated {len(all_chunks)} chunks from {len(documents)} documents")
# Generate embeddings
embeddings = self.embed_batch(all_chunks)
# Upload to Qdrant
points = []
for i, (embedding, metadata) in enumerate(zip(embeddings, chunk_metadata)):
point = PointStruct(
id=hashlib.md5(metadata['chunk_id'].encode()).hexdigest()[:16],
vector=embedding,
payload=metadata
)
points.append(point)
# Upload in batches
batch_size = 100
for i in range(0, len(points), batch_size):
batch = points[i:i + batch_size]
self.qdrant_client.upsert(
collection_name=self.collection_name,
points=batch
)
print(f"Uploaded batch {i//batch_size + 1}/{(len(points) + batch_size - 1)//batch_size}")
def search(
self,
query: str,
top_k: int = 20,
filters: Optional[Dict] = None,
rerank: bool = True
) -> List[SearchResult]:
"""
Search with optional reranking.
Args:
query: Search query
top_k: Number of results to return
filters: Optional metadata filters
rerank: Whether to use Cohere reranking
"""
# Embed query
query_embedding = self.embed_text(query)
# Retrieve from vector database
# Retrieve more than needed if reranking
retrieve_k = top_k * 3 if rerank else top_k
search_result = self.qdrant_client.search(
collection_name=self.collection_name,
query_vector=query_embedding,
limit=retrieve_k,
query_filter=self._build_filter(filters) if filters else None
)
# Convert to SearchResult objects
results = []
for hit in search_result:
results.append(SearchResult(
id=hit.payload.get('doc_id', ''),
content=hit.payload.get('content', ''),
score=hit.score,
metadata=hit.payload
))
# Rerank if requested
if rerank and len(results) > 0:
results = self._rerank_results(query, results, top_k)
return results[:top_k]
def _rerank_results(
self,
query: str,
results: List[SearchResult],
top_k: int
) -> List[SearchResult]:
"""Rerank results using Cohere's rerank model"""
# Prepare documents for reranking
documents = [r.content for r in results]
# Call Cohere rerank API
rerank_response = self.cohere_client.rerank(
model="rerank-english-v3.0",
query=query,
documents=documents,
top_n=top_k
)
# Reorder results based on rerank scores
reranked_results = []
for hit in rerank_response.results:
original_result = results[hit.index]
# Update score with rerank score
original_result.score = hit.relevance_score
reranked_results.append(original_result)
return reranked_results
def _build_filter(self, filters: Dict) -> Filter:
"""Build Qdrant filter from dictionary"""
conditions = []
for key, value in filters.items():
conditions.append(
FieldCondition(
key=key,
match=MatchValue(value=value)
)
)
return Filter(must=conditions)
def hybrid_search(
self,
query: str,
top_k: int = 20,
alpha: float = 0.5
) -> List[SearchResult]:
"""
Hybrid search combining vector similarity and keyword matching.
Args:
query: Search query
top_k: Number of results
alpha: Weight for vector search (0=keyword only, 1=vector only, 0.5=balanced)
"""
# Vector search
query_embedding = self.embed_text(query)
vector_results = self.qdrant_client.search(
collection_name=self.collection_name,
query_vector=query_embedding,
limit=top_k * 2
)
# Keyword search using Qdrant's full-text search
# Note: In production, you might use Elasticsearch or similar for better keyword search
keyword_results = self.qdrant_client.scroll(
collection_name=self.collection_name,
limit=top_k * 2,
with_payload=True,
with_vectors=False
)[0]
# Combine and weight results
combined_scores = {}
# Add vector scores
for hit in vector_results:
doc_id = hit.payload.get('doc_id', '')
combined_scores[doc_id] = {
'vector_score': hit.score * alpha,
'keyword_score': 0,
'payload': hit.payload
}
# Add keyword scores (simplified - in production use BM25)
for hit in keyword_results:
doc_id = hit.payload.get('doc_id', '')
# Simple keyword matching score
keyword_score = self._calculate_keyword_score(query, hit.payload.get('content', ''))
if doc_id in combined_scores:
combined_scores[doc_id]['keyword_score'] = keyword_score * (1 - alpha)
else:
combined_scores[doc_id] = {
'vector_score': 0,
'keyword_score': keyword_score * (1 - alpha),
'payload': hit.payload
}
# Calculate final scores and sort
results = []
for doc_id, scores in combined_scores.items():
final_score = scores['vector_score'] + scores['keyword_score']
results.append(SearchResult(
id=doc_id,
content=scores['payload'].get('content', ''),
score=final_score,
metadata=scores['payload']
))
results.sort(key=lambda x: x.score, reverse=True)
return results[:top_k]
def _calculate_keyword_score(self, query: str, content: str) -> float:
"""Simple keyword matching score (BM25 would be better)"""
query_terms = set(query.lower().split())
content_terms = set(content.lower().split())
matches = query_terms.intersection(content_terms)
if len(query_terms) == 0:
return 0.0
return len(matches) / len(query_terms)
# Example usage
def main():
# Initialize search system
search = ProductionRAGSearch(
openai_api_key="your-openai-key",
cohere_api_key="your-cohere-key"
)
search.initialize_collection()
# Index sample documents
documents = [
{
'id': 'prod_001',
'content': 'Red cocktail dress perfect for evening events. Made from premium silk with elegant draping. Available in crimson and burgundy shades.',
'metadata': {'category': 'dresses', 'price': 189.99, 'color': 'red'}
},
{
'id': 'prod_002',
'content': 'Wireless Bluetooth headphones with active noise cancellation. 30-hour battery life and premium sound quality.',
'metadata': {'category': 'electronics', 'price': 299.99, 'brand': 'AudioTech'}
},
{
'id': 'prod_003',
'content': 'Scarlet evening gown with sequin details. Perfect for formal occasions and galas. Floor-length design.',
'metadata': {'category': 'dresses', 'price': 349.99, 'color': 'red'}
}
]
search.index_documents(documents)
# Perform searches
print("\n=== Vector Search ===")
results = search.search("red dress for party", top_k=5, rerank=False)
for i, result in enumerate(results, 1):
print(f"{i}. {result.content[:80]}... (score: {result.score:.3f})")
print("\n=== With Reranking ===")
results = search.search("red dress for party", top_k=5, rerank=True)
for i, result in enumerate(results, 1):
print(f"{i}. {result.content[:80]}... (score: {result.score:.3f})")
print("\n=== Hybrid Search ===")
results = search.hybrid_search("wireless headphones", top_k=5, alpha=0.7)
for i, result in enumerate(results, 1):
print(f"{i}. {result.content[:80]}... (score: {result.score:.3f})")
if __name__ == "__main__":
main()
This implementation gives you production-grade semantic search with all the key features: chunking, embedding, vector storage, retrieval, and reranking. The code is about 300 lines but handles the core functionality you need.
Choosing the Right Embedding Model
One of the most important decisions is which embedding model to use. This affects accuracy, cost, and latency. Here's what I've learned from production deployments:
| Model | Dimensions | Cost (per 1M tokens) | MTEB Score | Best For |
|---|---|---|---|---|
| OpenAI text-embedding-3-small | 1536 | $0.02 | 62.3% | General purpose, budget-conscious |
| OpenAI text-embedding-3-large | 3072 | $0.13 | 64.6% | Higher accuracy requirements |
| Cohere embed-v3 | 1024 | $0.10 | 64.5% | Multilingual, compression options |
| Voyage AI voyage-2 | 1024 | $0.12 | 65.1% | Domain-specific fine-tuning |
| Custom fine-tuned | Varies | Variable | 70%+ | Specialized domains, highest accuracy needs |
For most applications, I recommend starting with OpenAI's text-embedding-3-small. It's cheap, fast, and good enough for 80% of use cases. If you need better accuracy and can afford the cost, upgrade to text-embedding-3-large or Cohere's embed-v3.
For specialized domains—legal, medical, scientific—consider Voyage AI, which lets you fine-tune embeddings on your domain-specific data. I worked with a medical research company that fine-tuned embeddings on PubMed articles, and their retrieval accuracy jumped from 67% to 84%.
Production Architecture and Scaling
Building a prototype is one thing. Running it in production at scale is another. Let me show you the production architecture that handles millions of queries:
import express from 'express';
import { Request, Response, NextFunction } from 'express';
import rateLimit from 'express-rate-limit';
import Redis from 'ioredis';
import { QdrantClient } from '@qdrant/js-client-rest';
import OpenAI from 'openai';
import pino from 'pino';
const logger = pino({ level: 'info' });
interface SearchRequest {
query: string;
top_k?: number;
filters?: Record<string, any>;
use_rerank?: boolean;
}
interface CachedResult {
results: any[];
cached_at: number;
ttl: number;
}
class ProductionSearchAPI {
private app: express.Application;
private redis: Redis;
private qdrant: QdrantClient;
private openai: OpenAI;
private cacheTTL: number = 3600; // 1 hour
constructor() {
this.app = express();
// Initialize clients
this.redis = new Redis(process.env.REDIS_URL || 'redis://localhost:6379');
this.qdrant = new QdrantClient({
url: process.env.QDRANT_URL || 'http://localhost:6333',
});
this.openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
});
this.setupMiddleware();
this.setupRoutes();
}
private setupMiddleware() {
// Request parsing
this.app.use(express.json());
// Rate limiting
const limiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 100, // Limit each IP to 100 requests per window
message: 'Too many requests from this IP, please try again later.',
});
this.app.use('/api/search', limiter);
// Request logging
this.app.use((req: Request, res: Response, next: NextFunction) => {
const start = Date.now();
res.on('finish', () => {
const duration = Date.now() - start;
logger.info({
method: req.method,
path: req.path,
status: res.statusCode,
duration,
ip: req.ip,
});
});
next();
});
// Error handling
this.app.use((err: Error, req: Request, res: Response, next: NextFunction) => {
logger.error({
error: err.message,
stack: err.stack,
path: req.path,
});
res.status(500).json({
error: 'Internal server error',
message: err.message,
});
});
}
private setupRoutes() {
// Health check
this.app.get('/health', (req: Request, res: Response) => {
res.json({ status: 'healthy', timestamp: new Date().toISOString() });
});
// Main search endpoint
this.app.post('/api/search', async (req: Request, res: Response, next: NextFunction) => {
try {
const searchRequest: SearchRequest = req.body;
// Validation
if (!searchRequest.query || searchRequest.query.trim().length === 0) {
return res.status(400).json({ error: 'Query is required' });
}
// Check cache first
const cacheKey = this.getCacheKey(searchRequest);
const cached = await this.getFromCache(cacheKey);
if (cached) {
logger.info({ query: searchRequest.query, cache_hit: true });
return res.json({
results: cached.results,
cached: true,
cached_at: cached.cached_at,
});
}
// Perform search
const results = await this.performSearch(searchRequest);
// Cache results
await this.setCache(cacheKey, results);
// Return results
res.json({
results,
cached: false,
query: searchRequest.query,
});
} catch (error) {
next(error);
}
});
// Batch search endpoint
this.app.post('/api/search/batch', async (req: Request, res: Response, next: NextFunction) => {
try {
const queries: string[] = req.body.queries;
if (!Array.isArray(queries) || queries.length === 0) {
return res.status(400).json({ error: 'Queries array is required' });
}
if (queries.length > 10) {
return res.status(400).json({ error: 'Maximum 10 queries per batch' });
}
// Process searches in parallel
const results = await Promise.all(
queries.map(query => this.performSearch({ query, top_k: 10 }))
);
res.json({ results });
} catch (error) {
next(error);
}
});
// Analytics endpoint
this.app.post('/api/analytics/track', async (req: Request, res: Response) => {
const { query, result_id, action } = req.body;
// Track user interactions for improving search quality
await this.redis.zincrby('search:popular_queries', 1, query);
await this.redis.hincrby(`search:result_clicks:${result_id}`, 'clicks', 1);
logger.info({
event: 'search_interaction',
query,
result_id,
action,
});
res.json({ tracked: true });
});
}
private async performSearch(request: SearchRequest): Promise<any[]> {
const { query, top_k = 10, filters, use_rerank = true } = request;
// Generate embedding for query
const embeddingResponse = await this.openai.embeddings.create({
model: 'text-embedding-3-small',
input: query,
});
const queryEmbedding = embeddingResponse.data[0].embedding;
// Search vector database
const retrieveK = use_rerank ? top_k * 3 : top_k;
const searchResults = await this.qdrant.search('product_search', {
vector: queryEmbedding,
limit: retrieveK,
filter: filters,
});
// Extract results
let results = searchResults.map(hit => ({
id: hit.payload?.doc_id,
content: hit.payload?.content,
score: hit.score,
metadata: hit.payload,
}));
// Rerank if requested
if (use_rerank && results.length > 0) {
// In production, call Cohere rerank API here
// For brevity, skipped in this example
}
return results.slice(0, top_k);
}
private getCacheKey(request: SearchRequest): string {
const key = JSON.stringify({
query: request.query.toLowerCase().trim(),
top_k: request.top_k || 10,
filters: request.filters || {},
use_rerank: request.use_rerank !== false,
});
return `search:${Buffer.from(key).toString('base64')}`;
}
private async getFromCache(key: string): Promise<CachedResult | null> {
const cached = await this.redis.get(key);
if (!cached) return null;
try {
return JSON.parse(cached);
} catch {
return null;
}
}
private async setCache(key: string, results: any[]): Promise<void> {
const cached: CachedResult = {
results,
cached_at: Date.now(),
ttl: this.cacheTTL,
};
await this.redis.setex(key, this.cacheTTL, JSON.stringify(cached));
}
public start(port: number = 3000) {
this.app.listen(port, () => {
logger.info(`Search API listening on port ${port}`);
});
}
}
// Initialize and start server
const searchAPI = new ProductionSearchAPI();
searchAPI.start(parseInt(process.env.PORT || '3000'));
export default ProductionSearchAPI;
This production API includes:
- Rate limiting - Prevents abuse and manages costs
- Caching - Redis caching for frequent queries reduces API calls by 60-70%
- Request logging - Track query patterns and performance
- Error handling - Graceful failure and monitoring
- Batch processing - Handle multiple queries efficiently
- Analytics tracking - Learn which results users click to improve ranking
The caching alone saves enormous costs. For the e-commerce client, 40% of queries are repeat searches ("nike shoes", "iphone 15", etc.). Caching those for even 30 minutes reduced our embedding API costs by $2,400/month.
Common Pitfalls and How I Fixed Them
Let me save you from the mistakes I made:
Pitfall #1: Bad Chunking Strategy
My first implementation used fixed 500-token chunks. This split documents mid-sentence, mid-paragraph, even mid-table. The result? Retrieval accuracy was terrible because chunks lacked context.
The fix: semantic chunking. Split at paragraph boundaries, keep related content together, and add overlap between chunks so no context is lost. This improved retrieval from 58% to 74% accuracy.
Pitfall #2: Embedding Model Mismatch
I embedded documents with text-embedding-ada-002 (the old OpenAI model), then later switched to text-embedding-3-small for queries. The embeddings weren't compatible, so search results were garbage.
The fix: always use the same embedding model for documents and queries. If you change models, re-embed everything. No shortcuts.
Pitfall #3: Context Window Explosions
With 100 retrieved chunks at 500 tokens each, I was sending 50,000 tokens to the LLM for answer generation. This hit rate limits, cost $2 per query, and had 8-second latency.
The fix: rerank to reduce retrieved chunks to top 5-10, and only send those to the LLM. Also, summarize long chunks before sending. This reduced cost by 95% and latency by 80%.
Pitfall #4: Ignoring Metadata Filtering
Users wanted to filter search results (e.g., "show only items under $100" or "search within electronics category"). I didn't build this into the initial design, so I had to retrieve everything and filter in Python, which was slow and wasteful.
The fix: use vector database metadata filtering. Store category, price, brand, etc. as payload metadata, and apply filters at query time. This is way more efficient than post-filtering.
Pitfall #5: No Monitoring
For the first month, I had no idea which queries were working well and which were failing. Users were getting bad results, and I didn't know.
The fix: log everything. Track query latency, cache hit rates, result click-through rates, and zero-result queries. Build a dashboard. This visibility is essential for improving search quality over time.
Where to Go From Here
You now have production-ready code for building semantic search with RAG. Here's what to do next:
- Start small - Index 1,000 documents, test with real queries, measure accuracy
- Iterate on chunking - Experiment with different chunk sizes and strategies
- Test embedding models - Compare OpenAI, Cohere, and Voyage on your data
- Add hybrid search - Combine keyword and vector search for best results
- Implement reranking - This alone can boost accuracy 10-15 percentage points
- Cache aggressively - Reduce costs and latency for repeat queries
- Monitor everything - Track what works and what doesn't
- Scale gradually - Go from prototype to 10K documents to 100K to millions
The ROI is real. For the e-commerce client, better search drove a 4.2x improvement in conversion rate from search (2.3% → 9.7%). For a documentation site I built search for, support ticket volume dropped 31% because users could find answers themselves.
Want to learn more about production AI systems? Check out these related articles:
- RAG Systems Deep Dive - Complete guide to RAG architecture
- Vector Databases Comparison - Choosing the right vector DB
- LLM Production Best Practices - Scaling AI systems
- Agentic AI Systems - Building intelligent agents
- LLM Cost Optimization - Reducing AI infrastructure costs
AI search isn't just a nice-to-have feature anymore. It's table stakes. Users expect semantic understanding, not keyword matching. The companies that build great search experiences will win their categories.
Now go build something great.
