LLMs And Conversation Behavior In DialNexa

Compare DialNexa LLM choices such as OpenAI, Google, and Groq, and understand prompts, temperature, fallback LLMs, predictive preprocessing, functions, and rate previews.

LLMs in DialNexa are the part of a Voice AI call that decides what to say or do next. The selected model reads the prompt, conversation history, dynamic variables, knowledge context, and function definitions, then returns a reply or an action. If the transcript is correct but the answer is wrong, this is where the investigation usually starts.

The model is not a mind reader. If the outcome matters, write the instruction and define the field. Vibes are not a configuration format.

Model Families In DialNexa

Your workspace may show a subset of model families depending on what is enabled.

Model family	Use it when	Validate before production
OpenAI	You want the safest general-purpose default for prompts, functions, structured outputs, summaries, and post-call extraction.	Function arguments, response format, refusal wording, and behavior on long conversation history.
Google	You want to compare reasoning, long-context behavior, or performance on your specific knowledge content.	Tool calling, summary consistency, and strict instruction following.
Groq	You need fast model responses or a low-latency fallback where enabled.	Response length, function behavior, and whether speed still leaves enough reasoning quality.

For provider background where a catalog page exists, see OpenAI. For business-system actions the model can trigger or prepare, start with using integrations in agents.

What The Model Selector Does

Behavior	What users should know
Default model	New agents try to select GPT-4o Mini when it is available, otherwise the first available non-deleted model.
Pricing preview	The model selector can show ₹x.xx/min beside each model.
Published state	Published versions can disable model changes. Edit a draft when comparing models.
Provider logos	The selector visually distinguishes OpenAI, Google, and Groq model families.
Settings button	The settings popover controls temperature, fallback LLM, fallback delay, and predictive preprocessing.

Temperature

The LLM Temperature slider runs from 0 to 1 in the dashboard. Lower values are better for function calls and structured results.

Call type	Suggested direction
Booking, payments, eligibility, compliance, or structured extraction.	Keep temperature low. Stable arguments matter more than colorful phrasing.
Support intake or objection handling.	Start low, then test a moderate value only if responses are too stiff.
Knowledge-heavy calls.	Keep temperature low until retrieval and answer quality are proven.
Regulated scripts.	Keep temperature low and write explicit allowed and disallowed behavior.

Fallback LLM

Fallback LLM is a per-agent setting that can start a backup model after a configured delay. The default delay value in the dashboard is 500 ms when no saved value exists.

1. Pick a strong primary model

   Fallback is not a license to choose a weak primary. Start with the model that best follows your instructions.

2. Enable fallback for latency, not decoration

   Use fallback when model response delay is a real caller problem.

3. Set the delay

   A short value such as 500 ms is a practical starting point. Lower values can race too often. Higher values may be too late to help.

4. Choose the fallback model

   When fallback is enabled and nothing is selected, the dashboard tries to pick an available fast fallback option where possible.

5. Check call evidence

   Review whether fallback won, whether the response was correct, and whether the user actually felt less delay.

Predictive Preprocessing

Predictive preprocessing can pre-generate likely replies between turns for non-flow agents. The toggle is not shown for Conversational Flow Agents because flow behavior is explicit node logic.

Good fit	Poor fit
Repeated scripts, reminders, confirmations, and predictable objection paths.	Calls where the next line depends on a custom function result.
Agents with stable prompts and low variation between calls.	Agents with many dynamic variables in almost every sentence.
Short replies that commonly repeat.	Long exploratory conversations.

Model Problems And First Fixes

The agent does not call functions correctly

Lower temperature, then improve function descriptions, required fields, examples, and error handling. Change model only after the function schema is clear.

The agent gives shallow answers

Check whether knowledge content was retrieved and whether the prompt asks for the right depth. Then compare OpenAI and Google on the same call script.

The agent is slow

Confirm whether delay comes from transcription, model generation, custom functions, text to speech, or telephony. Use fallback LLM only when the model is actually the slow part.

The agent changes behavior between calls

Reduce temperature, tighten instructions, and remove conflicting prompt sections. Then compare models with the same test script.

How Model Behavior Affects Integrations

When an agent calls a function or prepares data for a workflow, the model is responsible for deciding when the action is appropriate and which values are safe to pass.

User goal	Model responsibility	Read next
Book or reschedule something.	Ask for missing fields before calling the booking action.	Functions, Google Calendar.
Update a CRM.	Separate confirmed caller facts from guesses.	HubSpot, Salesforce.
Send an email or WhatsApp message.	Avoid promising a message before the required recipient and content are known.	Email with Resend, WhatsApp with Wati.
Escalate a support case.	Summarize the issue, priority, and promised next step without inventing details.	Zendesk, Intercom, Slack.

Related Reading

Prompts And Welcome Messages

Write the instructions the model follows.

Functions

Give the model safe actions.

Custom Functions

Connect actions to your APIs.

Provider Selection Guide

Choose model, voice, and transcriber together.