> For the complete documentation index, see [llms.txt](https://docs.sprinthub.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sprinthub.com/en/topics/artificial-intelligence/ai-agents/legacy-ai-agents-notice/strategies/chatgpt-allow-the-model-to-take-control-of-the-conversation-to-facilitate-interaction-with-the-lead.md).
# ChatGPT - Allow the model to take control of the conversation to facilitate interaction with the lead
### Introduction
Sprinthub allows the **GPT** to take control of support to make interaction with customers easier. This feature can be configured to allow the model to start and resolve support sessions automatically based on the available information. GPT-controlled support can be adjusted according to the business’s needs, ensuring efficiency and personalization.
### How to Enable GPT Support Control
To activate this feature, simply check the option **"Allow support control"** within the model settings. When enabled:
* The GPT can start conversations automatically.
* It can answer questions and provide information based on the available context.
* It can close support sessions when the user’s questions have been resolved.
> **Note:** This feature is only available when the model is started from a chatbot within Sprinthub.
### Contextualization Examples for GPT
Contextualization is essential to ensure that GPT responds correctly to users. In Sprinthub, the level of contextualization can be adjusted according to the complexity of the support. Below are some examples of how to structure the contextualization:
#### **1. Starting the Support**
GPT can be configured to start a conversation with customers as soon as it detects an interaction or based on specific rules.
**Instruction example:**
> "Greet the user in a friendly way and ask how you can help. If the customer’s question is related to products, provide details based on the available information."
**Expected response:**
> "Hello! Welcome to our support. How can I help you today?"
#### **2. Closing the Support**
To ensure that the support is concluded correctly, GPT can be instructed to end the conversation when it notices that the user’s need has been met.
**Instruction example:**
> "If the customer indicates that their question has been resolved or that they have no more questions, close the support politely and offer additional assistance if needed."
**Expected response:**
> "I’m glad I could help! If you have any more questions in the future, I’ll be here. Have a great day!"
#### **3. Support for E-commerce**
**Model configuration:**
* Objective: Help customers search for products and purchase information.
* Restrictions: Do not answer questions that are not in the catalog.
* Instruction example:
> "Act as a sales assistant. If a customer asks about prices or product specifications, use the registered information. If the question is not in the database, respond with 'Humm... I’m not sure'."
#### **4. Technical Support**
**Model configuration:**
* Objective: Help users with technical questions about software.
* Restrictions: Answer only based on the provided documents.
* Instruction example:
> "Answer only based on the available technical manual. If the question is not covered, recommend that the user contact specialized support."
#### **5. Meeting Scheduling Assistant**
**Model configuration:**
* Objective: Schedule meetings based on the user’s availability.
* Restrictions: Do not make decisions without the customer’s confirmation.
* Instruction example:
> "Help the user schedule meetings by checking available times. Always ask whether the suggested time works before confirming."
### Conclusion
GPT-controlled support in Sprinthub enables efficient and personalized automation. By adjusting the contextualization correctly, it is possible to create intelligent assistants that improve the customer experience, ensuring accurate and relevant interactions. Assess your needs and configure your model to optimize support in Sprinthub!
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