The Infrastructure You Don't Have to Build
Building a production-grade RAG system is harder than it looks. We've solved the edge cases.
1. Ingestion Engine
Recursive web crawler that handles sitemaps, navigates JS-rendered content, cleans HTML noise, and respects canonicals.
2. Vector Pipeline
Smart text chunking strategy. High-dimensional embeddings stored in a specialized vector database for sub-100ms retrieval.
3. LLM Orchestration
Context window optimization to fit the most relevant chunks. System prompts hardened against prompt injection and hallucinations.
Why RAG beats Fine-Tuning
Instant Updates
Update your website, re-scrape, and the bot knows the new info immediately. No re-training required.
Traceability
RAG allows us to cite sources ("See page: Pricing"). Fine-tuned models hide their sources in weights.
Data Privacy
Your data stays in your isolated vector index. It isn't used to train a global model shared with others.
// The RAG Flow
async function getAnswer(question) {
// 1. Embed question
const queryVec = await embed(question);
// 2. Vector Search
const context = await db.search(queryVec);
// 3. Generate Answer
const answer = await llm.generate({
system: "Use ONLY the context below.",
context: context,
prompt: question
});
return answer;
}