Cost Optimization for LLM-Powered Applications
Cost optimization for LLM-powered applications — caching, model routing, batching, and observability strategies QuantaloomAI uses to keep inference spend predictable at scale.
Jul 20268 min read
Vector Databases for RAG: Practical Architecture Choices
Vector databases for RAG: practical architecture choices — managed vs self-hosted, hybrid search, tenancy, and ops patterns QuantaloomAI ships in production.
Jun 202610 min read
Reducing Hallucinations in Customer-Facing AI Systems
Reducing hallucinations in customer-facing AI systems with grounding, refusal design, validation layers, and eval gates QuantaloomAI ships before launch.
Apr 202610 min read
Prompt Engineering Is Not Enough: Evaluation Pipelines That Ship
Prompt engineering is not enough: evaluation pipelines that ship — golden sets, regression gates, and online sampling QuantaloomAI uses in production AI.
Mar 202610 min read
Building Observability into Production LLM Applications
Building observability into production LLM applications: traces, evals, cost metrics, and incident playbooks QuantaloomAI uses to keep AI systems reliable.
Mar 202610 min read
LLM Application Development: RAG, Fine-Tuning, and When to Use Each
LLM application development in 2026: when to use RAG vs fine-tuning, hybrid patterns, evals, and cost controls QuantaloomAI ships with production AI products.
Feb 202610 min read
How to Build Production-Ready AI Products in 2026
Learn how to build production-ready AI products in 2026 — evals, observability, UX guardrails, and deployment patterns QuantaloomAI ships with every launch.
Jan 202610 min read