Vapi.ai latency optimization

We need someone to optimize the usazge of vapi.ai here things to be done. 1. LLM Prompt & Architecture Optimization The largest bottleneck in voice AI latency is usually the time it takes the Large Language Model to think (Time-to-First-Token or TTFT). Streaming Responses: Mastery of handling server-sent events (SSE) and streaming token outputs. The model must stream words to the Text-to-Speech (TTS) engine rather than waiting for the whole paragraph to finish. Prompt Engineering for Speed: Skill in writing concise system prompts. Massive, over-engineered prompts slow down processing. They should know how to structure prompts to force immediate, short responses. Model Selection: Experience benchmarks comparing models (e.g., using DeepSeek, Groq, or OpenAI's real-time/turbo models) to pick the fastest brain for your specific use case. 2. Text-to-Speech (TTS) & STT Fine-Tuning The contractor needs to optimize the translation layers (Speech-to-Text and Text-to-Speech). Provider Routing: Knowledge of which providers (Deepgram, ElevenLabs, Play.ht, LMNT) offer the lowest latency for specific languages and regions. First-Token TTS Optimization: Configuring the system so the voice synthesis engine begins speaking the moment the first word is generated by the LLM, rather than waiting for full sentences.