Intel / article

How To Prompt A Conversational Ai System

Source: elevenlabs.io
Author: ElevenLabs editorial sitemap
Date: Published Sep 22, 2026

Why it matters

Voice agents fail when prompts copy employee handbooks. The piece shows how LLM defaults (verbose, wrong tone) mismatch speech and how to prompt and set guardrails against them.

Key takeaways · AI-distilled

Older phone-tree systems were clunky but contained, with few possible paths and easy , whereas LLMs open risks such as making impossible promises, getting angry with customers, or breaching sensitive data.
ElevenLabs attributes the tone mismatch to , which pushes LLMs toward verbose, with bullets and headlines instead of the concise, flattened style of spoken conversation.
LLMs tend to fill unknowns with inferred knowledge rather than asking questions, which can produce costly errors such as promised refunds; the piece recommends a knowledge base and guardrails to ground them.
Because function calls are not instantaneous, the should warn the caller whenever a delay is expected, replacing the old human-agent habit of stalling to buy time.
To prevent padding text, the article suggests giving the LLM an explicit output structure, such as instructing it to respond only with the exact string to be read aloud to the user.

Terms in this piece · Glossary

guardrails — The checks around a model that block bad inputs and outputs — filters, validators, and permission rules the model itself can't override.
RLHF — Reinforcement learning from human feedback — training a model to prefer answers humans rate as better, which turns a raw text predictor into a usable assistant.
structured output — Forcing a model's response to match a schema, so downstream code can parse it instead of guessing at prose.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.

Read the source elevenlabs.io