> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.uponai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom LLM Best Practices

> Tips for optimizing latency, accuracy, and naturalness in Custom LLM integrations.

## Prompt Engineering

* Keep prompts concise — longer prompts can harm performance
* For large knowledge bases, use RAG to filter only relevant information to each query
* Filler words and slight variation make agents sound more human-like
* When using function calling, lower temperature improves accuracy
* To constrain agent behavior, combine internal states (similar to an IVR tree) with different prompts and functions per state

<Note>
  For conversational AI, latency is critical. Chaining multiple LLM calls will hurt the experience.
</Note>

## LLM Selection

Check each provider's **latency** and **throughput** benchmarks. UponAI starts streaming at the first sentence, so:

> **time to first token + throughput of first sentence = what matters most**

## Response Style

* Keep responses short and concise
* Filler words and controlled variation make agents more human-like
* Aim for responses that fit naturally into a live phone call context