Large Language Models for Voice Agents: Choosing the Right LLM
How to select and optimize LLMs for AI voice agent applications. Covers latency, cost, accuracy, and production deployment.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
How to select and optimize LLMs for AI voice agent applications. Covers latency, cost, accuracy, and production deployment.
Building real-time AI applications with Claude -- SSE streaming, WebSocket bidirectional chat, and production latency optimization.
Compare CallSphere and Retell AI for AI voice agents. See features, pricing, compliance, and which platform is better for your business.
Cited, grounded AI is becoming the default. See where Claude citation systems are heading — per-claim, action-level provenance — and how to prepare now.
Citations can look right and be wrong. Measure claim-support rate, abstention, and citation precision to prove your grounded Claude system actually works.
A real end-to-end build of a Claude support agent that cites every answer — from messy knowledge base to shipped, monitored, span-grounded system.
Cited Claude answers can fail with confidence. Map the failure modes, contain the blast radius, and build verification and kill-switch guardrails.
Grounding Claude answers with citations reshapes hiring. Learn the new evidence-engineering, eval, and prompt skills your team must build to ship cited AI.
Scale citation-grounded Claude from one team to many: a shared retrieval platform, federated per-domain corpora, namespacing, and a mandatory eval gate.