How to Create a Product FAQ Assistant for Support Teams: A Detailed AirgapAI Training Guide

Become the support lead whose team resolves issues on the first call—consistently and confidently. This comprehensive guide is designed for support managers and teams, aiming to deliver faster, accurate answers to customer inquiries. You will learn how to leverage Iternal Technologies' AirgapAI and its patented Blockify technology to convert your existing knowledge bases and frequently asked questions into a powerful, private, and precise internal assistant. We will cover everything from initial setup to advanced workflow creation, including how to add different versions of information, set up escalation prompts, and build an interactive troubleshooting tree.

AirgapAI offers a revolutionary approach to artificial intelligence, providing 78 times greater accuracy than traditional methods and ensuring your customer data remains private and secure—all without requiring any prior artificial intelligence expertise. Get ready to empower your support team with a cutting-edge tool that enhances productivity and decision-making.

1. Understanding AirgapAI: Your Local, Secure Artificial Intelligence Assistant

Before we dive into creating your Frequently Asked Questions assistant, let's understand what AirgapAI is and how it revolutionizes the way your organization interacts with artificial intelligence.

At its core, AirgapAI is an offline, on-device large-language-model platform. This means it brings the power of advanced artificial intelligence directly to your personal computer, completely eliminating the need for a connection to external cloud services or the internet for its core operations. Unlike popular cloud-based artificial intelligence solutions, AirgapAI operates with 100 percent local artificial intelligence, ensuring unparalleled data sovereignty and security.

What is Artificial Intelligence (AI) and a Large Language Model (LLM)?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. It encompasses learning, reasoning, problem-solving, perception, and language understanding.

A Large Language Model (LLM) is a type of artificial intelligence program designed to understand and generate human language. Think of it as a highly sophisticated text prediction system that has learned from vast amounts of text data. When you ask an Large Language Model a question, it uses its training to generate a coherent and relevant response.

Why AirgapAI is Different: Security, Accuracy, and Cost-Efficiency

Traditional Large Language Models often rely on cloud infrastructure, meaning your data, even sensitive customer information, is sent over the internet to a third-party server for processing. This raises significant concerns about data privacy, data sovereignty, and security, especially for organizations dealing with confidential client information or operating in regulated industries.

AirgapAI addresses these challenges head-on:

Unparalleled Security and Data Privacy: By running entirely on your personal computer, AirgapAI ensures that your data never leaves your device. There's no network "in" or "out," making it ideal for high-security environments where data protection is paramount. This guarantees your private artificial intelligence and secure artificial intelligence needs are met.
Exceptional Accuracy with Blockify Technology: A common problem with artificial intelligence is "hallucinations"—where the model generates incorrect or nonsensical information. AirgapAI combats this with its patented Blockify technology, which refines data inputs to achieve a remarkable 7,800 percent improvement in Large Language Model accuracy. This means your support team will receive highly reliable and trusted answers for artificial intelligence technology, reducing the risk of misguidance.
Significant Cost Savings: AirgapAI offers a perpetual license model, meaning you pay a one-time fee per device. This is a dramatic departure from the expensive, ongoing subscription fees associated with cloud alternatives, saving your organization up to 15 times the cost. You get a no subscription artificial intelligence application and one device artificial intelligence license, making it a cost-effective artificial intelligence solution.
Offline Functionality: Since it's a local artificial intelligence solution, AirgapAI works seamlessly even without an internet connection. This offline artificial intelligence capability is crucial for field personnel, remote locations, or any scenario where network access is unreliable or restricted.
Easy Deployment and Management: AirgapAI is designed for the enterprise, integrating effortlessly into standard Information Technology imaging workflows. It's an executable application that can be deployed and updated by your Information Technology team just like any other software, making it an installable artificial intelligence software for Windows offline environments.

Now that you understand the powerful foundation of AirgapAI, let's prepare your system and begin the journey of transforming your support operations.

2. System Requirements and Installation: Getting AirgapAI Ready

To ensure a smooth experience with AirgapAI, your personal computer needs to meet certain specifications. AirgapAI is optimized to run on modern AI personal computers, leveraging the Central Processing Unit, Graphics Processing Unit, and Neural Processing Unit for optimal performance.

2.1. System Requirements

Here are the recommended specifications for running AirgapAI:

Component	Minimum	Recommended
Central Processing Unit	8 Cores	8 Cores / 16 Threads or better
Random Access Memory	16 Gigabytes	32 Gigabytes +
Disk Space	10 Gigabytes free (Solid State Drive)	50 Gigabytes Non-Volatile Memory Express
Graphics Processing Unit	4 Gigabytes + Video Random Access Memory	8 Gigabytes + Video Random Access Memory
Operating System	Windows 11 (latest patches are always best)	Windows 11 (latest patches are always best)

Additionally, you will need security permissions on your system to install applications.

2.2. Downloading and Installing AirgapAI

The installation process for AirgapAI is straightforward and designed to be as simple as installing any other desktop application.

Obtain the Installer Package: Your Information Technology department will provide you with the latest compressed file (ZIP archive) containing the AirgapAI installer. Save this file to a readily accessible location, such as your Downloads folder.
- Example File: AirgapAI-v1.0.2-Install.zip
Extract the Files: Right-click on the downloaded compressed file (e.g., AirgapAI-v1.0.2-Install.zip) and select "Extract All..." from the context menu. Choose a destination folder (the default is usually a new folder within your Downloads directory) and click "Extract."
Run the Installer: Open the newly extracted folder. Inside, you will find the AirgapAI Chat Setup executable file. Double-click on AirgapAI Chat Setup.exe to begin the installation wizard.
Follow the Installation Wizard:
- Accept the license agreement.
- Choose to create a Desktop Shortcut for easy access.
- Click "Install."
- Click "Finish" once the installation is complete.
Address Security Prompts (if any): If your operating system's security features (like SmartScreen) prompt you, select "Allow" or "Run anyway" to proceed with the installation.

Once installed, you can launch AirgapAI Chat from your desktop shortcut or Start menu entry.

3. First-Launch Onboarding Wizard: Setting Up Your Artificial Intelligence Assistant

Upon launching AirgapAI for the first time, the application will guide you through a simple onboarding process. This wizard helps you configure your profile, select your preferred chat style, and upload the necessary Large Language Models and datasets.

3.1. Profile and Chat Style

Start Onboarding: When AirgapAI Chat launches, it will detect that no models are present and initiate the Onboarding flow. Click "Start Onboarding."
Enter Display Name: You'll be prompted to enter a display name. This is how you'll be identified in chat conversations. The default is "You."
Pick a Chat Style: Select a visual theme for your chat interface. Options might include "Iternal Professional," "Casual," "Dark Mode," or "Retro." Choose the one that suits your preference.
Click Next.

3.2. Uploading the Core Large Language Model

The core Large Language Model is the brain of your artificial intelligence assistant. AirgapAI supports various open-source models, and you'll choose one optimized for your personal computer's hardware.

Models Screen: On the "Models" screen, expand the "Available Models" drop-down menu. It will initially be empty.
Click Upload Model.
Browse to Model Folder: Navigate to the /models/ folder within the directory where you extracted the installer package in Section 2.2.
Choose a Model: Select a Large Language Model that aligns with your hardware specifications:
- Llama-1B (suitable for 2024 Integrated Graphics Processing Unit or lower-power devices)
- Llama-3B (ideal for Integrated Graphics Processing Units from 2025 or dedicated Graphics Processing Units)
Click Save. The upload process will take approximately 30 seconds.

Note: Information Technology administrators can manage and update Large Language Models by accessing the C:\Users\YourUsername\AppData\Roaming\IternalModelRepo folder after the initial model upload.

3.3. Uploading an Embeddings Model

An embeddings model is crucial for helping the Large Language Model understand the meaning and context of your data, especially for Retrieval-Augmented Generation (RAG)—the process of using your own data to inform artificial intelligence responses.

Still on Onboarding Page: While still on the onboarding page, click "Upload Embeddings Model."
Select Embeddings Model: Open the /models/ folder again and select Jina-Embeddings.zip.
Click Save. This upload will also take around 30 seconds.

Note: Information Technology administrators can manage and update Embeddings Models by accessing the C:\Users\YourUsername\AppData\Roaming\IternalModelRepo folder after the initial model upload.

3.4. Adding Sample or Custom Datasets

Datasets are the foundation of your custom Frequently Asked Questions assistant. They provide the specific knowledge your artificial intelligence will use to answer questions accurately.

Click Upload Dataset.
Navigate to Datasets Folder: Go to the /datasets/ folder from your extracted installer.
Select a Sample Dataset: Choose CIA_World_Factbook_US.jsonl as a sample to get started.
Click Save.

Note: Information Technology administrators can update loaded datasets by modifying the contents of the files saved within C:\Users\YourUsername\AppData\Roaming\airgap-ai-chat\CorpusRepo.

Tip: While you can upload standard documents (like Word, PDF, or Text files) directly, for maximum accuracy (approximately 78 times greater), it is highly recommended to convert larger bodies of information into Blockify datasets. The local, on-device Blockify tool will be available in Quarter 3 of 2025.

3.5. Finish Onboarding

Verify Additions: Confirm that all three items (Core Large Language Model, Embeddings Model, and at least one Dataset) have been successfully added.
Click Continue. AirgapAI Chat will now boot up with your selected configurations.

3.6. Optional Setup Steps for Information Technology Teams

For Information Technology System Administrators who wish to integrate AirgapAI Chat with Dell Technologies' Dell Pro Artificial Intelligence Studio, follow these optional steps:

Dell Technologies Dell Pro Artificial Intelligence Studio Support

AirgapAI Chat supports native integration with Dell Technologies’ Dell Pro Artificial Intelligence Studio (often abbreviated as DPAIS).

Install Required Files: As the Information Technology System Administrator, install the necessary files to enable an Large Language Model via Dell Pro Artificial Intelligence Studio. Both Intel and Qualcomm processor architectures are supported.
Validate Local Large Language Model Application Programming Interface Endpoints: After the Dell Pro Artificial Intelligence Studio services are running, validate that the local Large Language Model Application Programming Interface endpoints can be successfully called.

Set Environment Variable: Open Powershell and input the following command:

[System.Environment]::SetEnvironmentVariable("DPAIS_ENDPOINT", "http://localhost:8553/v1/openai", "User")

Relaunch AirgapAI Chat: Relaunch the AirgapAI Chat application. The Dell Pro Artificial Intelligence Studio Large Language Models available will automatically appear in the model selection menu within the settings page.

4. Initial Model Benchmarking: Optimizing Performance

After your first model launch, AirgapAI Chat will offer to benchmark your personal computer's hardware. This step is highly recommended as it measures your system's processing capabilities and optimizes the artificial intelligence's performance for your specific device.

Run Benchmark: When prompted, click "Run Benchmark."
Duration: The benchmark typically takes approximately two minutes. It measures key performance indicators such as tokens per second and inference speed.
Context-Window Expansion: While you can skip this step, if you do, your context-size limits will remain at a conservative 2,000 tokens. Completing the benchmark allows you to expand the token context window later by visiting "Settings" > "Chat" and adjusting the slider to your desired size, up to 32,000 tokens for advanced models.

5. Building Your Support FAQ Assistant with Blockify Technology: "Docs to Blocks"

This is where the magic happens for your support team. AirgapAI's patented Blockify technology transforms your existing knowledge base, frequently asked questions, and troubleshooting guides into a highly accurate, easily searchable, and secure dataset for your artificial intelligence assistant. This process is crucial for achieving the 78 times greater artificial intelligence accuracy that AirgapAI promises.

5.1. Understanding Blockify Technology: The Foundation of Accuracy

Blockify is more than just a data ingestion tool; it's a data management solution specifically designed for Large Language Models at scale. It creates a "single source of truth" structure, optimized for your artificial intelligence to answer questions with extreme precision and virtually eliminate hallucinations.

The Blockify Process Explained:

Imagine you have thousands of support documents, manuals, and frequently asked questions. Blockify does the following:

Ingestion: It ingests vast datasets from various formats (text, Hypertext Markup Language, Portable Document Format, Word documents, PowerPoint presentations, and even graphic files). For video content, it extracts still frames or transcribes audio.
Condensation and Distillation: Blockify then condenses these documents into concise, modular units called "blocks." Each block is carefully structured to provide the most critical information.
Structured Blocks: Each block typically includes:
- A Name: (Displayed in blue within the Blockify interface) This quickly identifies the content topic (e.g., "Printer Troubleshooting Guide," "Refund Policy for Product X").
- A Critical Question: (Bold, italicized) This is the key query a customer or support agent might ask, designed to elicit the most relevant information from the block.
- A Trusted Answer: (Light gray) This is the distilled, accurate, and approved response, avoiding outdated or redundant information.
Rich Metadata Tagging: Each block is tagged with rich metadata, including classification, permissions, and classification levels. This is vital for zero-trust environments and ensures that sensitive information is only accessible to authorized users. This enables private artificial intelligence and secure artificial intelligence for confidential chats.
Dramatic Data Reduction: This process can reduce the original data size by as much as 97.5 percent (down to 2.5 percent of the original content).
Unmatched Accuracy Improvement: Most importantly, this meticulous process improves the accuracy of your Large Language Models by a staggering 7,800 percent (78 times), significantly reducing the likelihood of artificial intelligence hallucinations.

5.2. Your Workflow: From "Docs" to "Blocks"

Let's walk through the steps to convert your existing support documentation into Blockify datasets, creating a powerful internal assistant for your team.

Gather Your Data Sources: Collect all relevant support materials:
- Frequently Asked Questions (FAQs) documents
- Knowledge Base articles
- Product manuals and specifications
- Internal troubleshooting guides
- Customer service scripts
- Legal and compliance documents related to support
- Any other documents your support team relies on for accurate answers.
- Tip: Curate your data into relevant categories (e.g., specific product lines, common issue types) to leverage AirgapAI's hierarchical metadata framework effectively.
Ingest Documents into Blockify (Future Feature - Local Q3 2025): While the full local Blockify application will be available in Q3 2025, the workflow remains consistent. Imagine the following steps:
- Open the Blockify application.
- Create a New Task: Start a new task (e.g., "Support FAQ Dataset").
- Upload Your Documents: Select and upload your collected documents. Blockify will automatically begin extracting key blocks.
- Observe Block Creation: The interface will display the blue block names, bold/italicized critical questions, and light gray trusted answers as they are generated.
Human Review and Curation: Ensuring Quality and Adding Versions:
- Review Process: After automatic ingestion, these blocks are sent for human review. This is a critical step to ensure the quality and accuracy of your dataset.
- Update or Approve Messaging: Your team (subject matter experts, support leads) will review the generated blocks. This is where you can:
  - Correct Inaccuracies: Adjust any answers that are not perfectly clear or concise.
  - Flag Outdated Content: Identify and remove or update information that is no longer current (e.g., a policy from 2019 that has since changed).
  - Refine Questions: Ensure the "Critical Questions" are phrased in a way that accurately reflects how a support agent would ask for the information.
  - Add "Versions": For frequently updated documents (like policy changes or product updates), you can create new Blockify tasks and incorporate the latest versions. The system's metadata tagging allows you to classify these versions and ensure the artificial intelligence pulls the most current "single source of truth." When new documents are Blockified, the datasets can be updated and pushed to local devices via Microsoft Intune or similar image management applications.
Export and Load Blockify Datasets: Once reviewed and approved, your refined Blockify datasets are ready to be loaded into AirgapAI Chat. Your Information Technology team can push these updated datasets to the local devices of your support agents.

This meticulous "Docs to Blocks" process is the cornerstone of building a truly reliable and highly accurate artificial intelligence assistant for your support team.

6. Building a "Troubleshooting Tree" and Setting Escalation Prompts with AirgapAI Workflows

Now that your data is Blockified and loaded into AirgapAI, let's configure workflows to create a dynamic "troubleshooting tree" and intelligent escalation prompts for your support team. AirgapAI's "Quick Start Workflows" and "Entourage Mode" are powerful tools for this.

6.1. Leveraging Quick Start Workflows for Guided Support

AirgapAI includes "Quick Start Workflows" that can be tailored for different roles and scenarios. For a support team, these can guide agents through common troubleshooting steps or provide instant access to specific types of information.

Access Workflow Templates:
- In the AirgapAI Chat application, navigate to "Settings" (usually an icon like a gear or wrench).
- Go to the "Workflows" tab. Here, Information Technology administrators can add or edit prompt chains—pre-configured questions or instructions that guide the artificial intelligence's response.
Create Support-Specific Workflows:
- Common Issue Troubleshooting: Develop workflows for frequent customer issues.
  - Example Workflow Name: "Printer Offline Troubleshooting"
  - Example Prompt Chain: "Customer reports printer is offline. Ask the customer: 1. Is the printer plugged in? 2. Is it connected to Wi-Fi? 3. What error message is displayed? Based on the answers, provide the most likely solution from the 'Printer Support' dataset."
- Product Feature Inquiry: Create workflows for common questions about product features.
  - Example Workflow Name: "Product X Feature Q&A"
  - Example Prompt Chain: "Customer is asking about Feature Y of Product X. Access the 'Product X Manual' dataset and summarize how Feature Y works, including common setup steps and benefits."
- Policy Lookup: Instant access to company policies.
  - Example Workflow Name: "Return Policy Lookup"
  - Example Prompt Chain: "Customer is asking about the return policy for a purchased item. Access the 'Company Policies' dataset and provide the return period, conditions for return, and steps for initiating a return."
Role-Based Segmentation:
- Remember, AirgapAI can be tied to a user's profile on login. This means different support roles (e.g., Tier 1, Tier 2, Billing Support) can have their own pre-configured prompts and access to specific curated datasets, ensuring they only see information relevant to their responsibilities. This is configured through your standard image and provisioning process by your Information Technology team.

6.2. Setting Escalation Prompts: Knowing When to Hand Off

An effective support assistant doesn't just provide answers; it also helps agents know when an issue is beyond their scope and needs to be escalated. You can embed these "escalation prompts" directly into your workflows or train the artificial intelligence to recognize certain triggers.

Embed Escalation Logic in Workflows:
- Within your workflow prompt chains, you can include conditional logic.
- Example for "Printer Offline Troubleshooting": "If the customer has confirmed power and Wi-Fi, and the error message is 'Hardware Malfunction 0x00FF,' this indicates a Tier 2 hardware issue. Provide the Tier 2 escalation procedure and the relevant internal ticket category."
Define Escalation Triggers within Datasets:
- In your Blockify datasets, you can tag certain "Trusted Answers" with an "Escalate" metadata tag. The artificial intelligence can then be prompted to look for these tags.
- Example "Critical Question": "When should a support ticket be escalated to Tier 2?"
- Example "Trusted Answer": "Escalate to Tier 2 if the issue involves hardware diagnostics, network configuration changes beyond basic troubleshooting, or persistent software bugs requiring developer input. Always ensure basic troubleshooting steps have been completed and documented before escalation."
Create a Dedicated Escalation Workflow:
- Example Workflow Name: "Escalate Issue"
- Example Prompt Chain: "I need to escalate a customer issue. The customer's problem is [describe problem]. I have already tried [list steps]. Based on this, provide the appropriate escalation path (Tier 2, Engineering, Billing), required documentation, and contact person/team."

6.3. Building an Interactive "Troubleshooting Tree" with Artificial Intelligence

Instead of a static flow chart, AirgapAI can help build a dynamic, interactive troubleshooting experience.

Structured Question & Answer Datasets: Ensure your Blockify datasets for troubleshooting are structured with clear "Critical Questions" and "Trusted Answers" that guide the agent through diagnostic steps.
- Example Block Names: "Common Printer Issues," "Network Connectivity Checks," "Software Reinstallation Steps."
Iterative Prompting: Support agents can use the artificial intelligence to guide them step-by-step.
- Agent: "Customer has no internet connection."
- AirgapAI (from dataset): "First, check if the Wi-Fi adapter is enabled. Is it?"
- Agent: "Yes, it's enabled."
- AirgapAI: "Next, perform a network reset. Did that resolve the issue?"
- ...and so on, until a solution is found or an escalation is triggered.
File Upload for Diagnostics: Agents can drag and drop customer log files or error reports (e.g., as PDF or text files) directly into the AirgapAI chat window.
- Agent: (Uploads error_log.txt) "Summarize this error log for a customer's software crash."
- AirgapAI Chat: (Embeds and summarizes instantly) "The log indicates a 'memory allocation failure' in the 'Graphics Driver X' module, occurring at [timestamp]."
- Agent: "Based on this, what's the recommended fix?"
- AirgapAI Chat: (Fetches from Blockify dataset) "For 'memory allocation failure' in 'Graphics Driver X,' update the graphics driver to the latest version, clear temporary files, and check system Random Access Memory utilization. Steps outlined in 'Graphics Driver Update Guide' block."

By combining these workflow and data strategies, your support team will have an incredibly powerful and accurate assistant that reduces resolution times, improves consistency, and ensures proper escalation when needed.

7. Everyday Workflows: Using AirgapAI Chat as Your Support Assistant

Once your AirgapAI is installed, onboarded, and loaded with Blockify datasets and workflows, your support team can begin using it for their daily tasks. AirgapAI Chat offers various modes to enhance productivity and decision-making.

7.1. Retrieval-Augmented Questions and Answers (Blockify Dataset)

This is the core functionality for your Frequently Asked Questions assistant. When a customer asks a question, your agents can quickly get accurate answers from your curated data.

Toggle Dataset On: In the sidebar of the AirgapAI Chat interface, ensure your relevant Blockify dataset (e.g., "Customer Support FAQ" or "Product Troubleshooting Guide") is toggled "ON." You can select specific datasets here.
Ask a Question: Type your query into the chat window. This could be a customer's exact question or an internal query for information.
- Example Question: "What is the warranty period for the 'ProSound Headphones'?"
- Example Question: "How do I reset the password for a user account?"
Receive Answer with Citations: The Retrieval-Augmented Generation engine will fetch relevant "IdeaBlocks" from your selected dataset, then the Large Language Model will synthesize a coherent, trusted answer. Crucially, AirgapAI will often show citations, indicating which specific blocks of your data were used to formulate the response, allowing agents to verify the source if needed.

7.2. File Upload and Summarization

Support agents often need to quickly understand the gist of a customer's provided document (e.g., an email chain, a technical report, or a contract).

Upload File: Drag and drop a supported file type (.pdf, .docx, .txt) directly onto the chat window, or click the paperclip icon (📎) to browse for a file.
Prompt for Summarization: Once uploaded, prompt AirgapAI Chat to summarize the document.
- Prompt Example: "Summarize this document in bullet points, focusing on the customer's main complaints."
- Prompt Example: "Extract the key terms from this contract."
Instant Summary: AirgapAI Chat embeds and summarizes the content instantly, providing quick insights without the agent needing to read through lengthy documents.

7.3. Guided Demo Workflows for Standard Procedures

Your previously configured "Quick Start Workflows" (see Section 6.1) are now readily available.

Select Workflow: On the Workflow Bar (typically located below the new chat window), select a relevant workflow.
- Example: "Printer Offline Steps"
Follow Prompts/Upload Documents: The workflow might ask for minimal input (e.g., "Customer's Name") or require uploading a supporting document.
Receive Engineered Output: AirgapAI will provide a structured response based on the workflow's instructions and your Blockified data. This could be a series of troubleshooting steps, a template email to the customer, or an internal memo.
Copy Output: Click the "Copy" icon (📋) to place the text directly onto your clipboard for easy pasting into a customer communication or internal ticket.

7.4. Entourage Mode (Multi-Persona Chat) for Complex Scenarios

For highly complex customer issues that require multiple perspectives or expertise, AirgapAI's "Entourage Mode" is invaluable. This feature allows users to interact with multiple artificial intelligence personas simultaneously, each drawing from potentially different datasets or configured with specialized knowledge.

Select Entourage Mode Workflow: From the new chat page, select an "Entourage Mode" quick start workflow.
Configure Personas: In "Advanced Settings" → "Personas," you can define and configure various artificial intelligence personas.
- Example Personas for Support: "Hardware Expert" (tuned on hardware manuals), "Software Specialist" (tuned on software documentation), "Billing Specialist" (tuned on billing policies), "Customer Experience Advocate" (focused on customer satisfaction guidelines).
- Scenario Example: When a customer has an issue involving a complex product setup, billing discrepancies, and a software bug, an agent could simultaneously query "Hardware Expert," "Software Specialist," and "Billing Specialist."
Ask a Question: Pose your question to the "Entourage."
- Recommended Prompt Example: "A customer is experiencing frequent disconnections after a recent software update. They are also questioning their last billing statement regarding a service fee. How should I proceed, considering both the technical issue and the billing concern?"
Receive Multi-Perspective Responses: Responses from each active persona will appear in a queue, allowing the agent to consider different expert viewpoints on the complex issue. A persona activity indicator will show which personas are currently generating their responses. This helps in high-stakes decision-making and scenario planning by combining diverse expert viewpoints.

7.5. Multilingual Conversations

If your support team handles international customers, AirgapAI Chat can assist with multilingual interactions.

Prompt in Desired Language: Simply ask your question or instruct the artificial intelligence in the desired language.
- Prompt Example: "Tell me how to troubleshoot a Wi-Fi connection in Spanish."
Seamless Language Switching: The Large Language Model will understand and respond in the specified language, enabling multilingual conversations.
Halt Generation: Use the "Stop" button to halt the artificial intelligence's generation at any time if the response is too long or veering off-topic.

By integrating these everyday workflows, your support team will transform into a highly efficient and accurate problem-solving unit, equipped with a powerful privacy-first artificial intelligence assistant that keeps sensitive customer data secure.

8. Ongoing Management and Support for AirgapAI

Maintaining your AirgapAI installation ensures your support team always has access to the latest features, models, and most importantly, the most up-to-date Blockified datasets.

8.1. Model and Dataset Management

Your Information Technology administrators play a key role in keeping AirgapAI current.

Large Language Model Updates: Information Technology can add or update Large Language Models by modifying the contents of the C:\Users\YourUsername\AppData\Roaming\IternalModelRepo folder. This allows for incorporating newer, more efficient, or specialized models as they become available.
Dataset Updates: As new documents are Blockified or existing policies change, updated datasets can be pushed to the local devices via Microsoft Intune or similar image management applications. Information Technology updates these by modifying the files saved within C:\Users\YourUsername\AppData\Roaming\airgap-ai-chat\CorpusRepo. This ensures your support Frequently Asked Questions assistant always has the most current and accurate information.

8.2. Advanced Configuration Options

AirgapAI offers several advanced settings for Information Technology teams or power users to fine-tune the experience:

Context-Window Expansion: After benchmarking, you can expand the artificial intelligence's memory of the conversation. Go to "Settings" → "Model Settings" and set "Max Tokens" up to 32,000, allowing for longer, more complex interactions.
Styling and Themes: Customize the application's appearance under "Settings" → "Appearance." You can switch between predefined themes or even build custom Cascading Style Sheets.
Workflow Templates: Beyond the initial setup, Information Technology can continuously refine and add new "prompt chains" under "Settings" → "Workflows," ideal for pre-loading company-specific tasks or new troubleshooting flows.
In-App Benchmarking Suite: Under "Settings" → "Benchmarking tab," you can re-test the performance of new or updated models on your hardware.

8.3. Updates and Maintenance

AirgapAI updates are delivered efficiently and can be managed by your Information Technology team.

Built-in Update Manager: Updates are handled by the application's built-in Update Manager. You can choose between a "Local Server" or "Cloud" in "Settings" → "Updates."
Update File Server Location: Information Technology can change the file server update location by modifying the updaterConfig.json file found at C:\Users\John\AppData\Local\Programs\AirgapAI Chat\resources\auto-updater\updaterConfig.json. This allows for secure internal deployment of updates.

8.4. Troubleshooting and Support

Should you encounter any issues, AirgapAI provides avenues for support.

Online Enablement Page: This page includes step-by-step videos, Frequently Asked Questions, user guides, and troubleshooting tips.
Customer Success Team: The Iternal Technologies customer success team is available for follow-up calls and additional workshops after initial deployment.
Direct Support: For additional questions or technical assistance, you can contact the product team at support@iternal.ai.

9. Conclusion: Empower Your Support Team with Trusted, Secure Artificial Intelligence

You have now completed a comprehensive guide on transforming your support operations with AirgapAI. From understanding the foundational concepts of artificial intelligence to meticulously crafting a powerful Frequently Asked Questions assistant with Blockify technology, you are equipped to empower your team.

AirgapAI delivers a swift artificial intelligence win, robust cost savings, and unparalleled data security, virtually eliminating all artificial intelligence hallucinations—all critical in today's challenging market. Its patented Blockify technology improves Large Language Model accuracy by an astounding 78 times, ensuring your team provides consistently accurate and reliable information. This means your data is not tracked, it is not shared, and your conversations remain private and confidential.

Take the next step to revolutionize your support workflow. Download the free trial of AirgapAI today and experience the future of secure, accurate, and local artificial intelligence firsthand.

Call to Action: Download the free trial of AirgapAI today at: https://iternal.ai/airgapai