The gap between an impressive AI demo and a production-grade AI feature is enormous. Most demos operate on clean data, with generous latency budgets, and no requirement for auditability. Production systems need deterministic fallbacks, cost controls, and integration with existing data governance. We focus on three patterns that reliably deliver value: retrieval-augmented generation, document classification, and workflow augmentation.
RAG over fine-tuning
For most business applications, retrieval-augmented generation outperforms fine-tuning. Your knowledge base changes frequently — policy documents update, product catalogues shift, internal procedures evolve. RAG lets you update the knowledge source without retraining. We chunk documents into semantic units, embed them with a model like text-embedding-3-small, and store vectors in pgvector alongside the source metadata.
// Simplified RAG pipeline
async function answerQuestion(query: string) {
const embedding = await embed(query);
const chunks = await db.query(
`SELECT content, metadata, 1 - (embedding <=> $1) as similarity
FROM documents
WHERE 1 - (embedding <=> $1) > 0.78
ORDER BY similarity DESC LIMIT 5`,
[embedding]
);
return llm.complete({
system: 'Answer using only the provided context.',
context: chunks.map(c => c.content).join('\n'),
query,
});
}Cost controls matter
An unmonitored LLM integration can generate surprising cloud bills. We implement per-user rate limits, cache frequent queries with semantic similarity matching, and route simple questions to smaller models. A tiered approach — small model for classification, large model for generation — typically reduces cost by 60% with minimal quality loss.
The most valuable AI features are the ones users forget are AI. They simply make the system faster, more accurate, and easier to use.