How to Reduce Artificial Intelligence Hallucinations by 78 Times Using Blockify

Become the Leader Whose Artificial Intelligence Actually Tells the Truth

In today's fast-paced digital landscape, Artificial Intelligence (AI) is transforming how businesses operate. Yet, a persistent challenge often undermines its potential: the phenomenon of Artificial Intelligence "hallucinations." These are instances where an AI system generates information that is plausible but factually incorrect or inconsistent with its training data. For organizations relying on AI for critical decision-making, this lack of reliability is a major barrier to adoption and trust.

However, with Iternal Technologies' AirgapAI and its patented Blockify technology, the paradigm shifts. "Trust but verify" transforms into "trust and scale," turning skepticism into widespread adoption. This detailed guide will walk you through understanding why Large Language Models (LLMs) hallucinate, how Blockify rigorously constrains answers, and how you can leverage AirgapAI to achieve a remarkable 7,800% (or 78 times) improvement in AI accuracy. This is not just marketing hype; it's a proven, proof-driven approach to reliable Artificial Intelligence.

This article is designed for Chief Information Security Officers (CISOs), compliance professionals, operations managers, and Information Technology (IT) departments who prioritize reliability, data governance, and absolute trust in their Artificial Intelligence solutions.

Understanding the Root Cause: Why Large Language Models Hallucinate

Before we dive into the solution, it's crucial to understand the problem. Large Language Models, the powerful Artificial Intelligence engines behind chat assistants, are trained on vast amounts of data from the public internet. While this enables them to generate human-like text, it also introduces several challenges that lead to hallucinations:

1. Messy Enterprise Data: Your company's internal data, while valuable, is often inconsistent, outdated, or stored in disparate formats. When an Artificial Intelligence model attempts to synthesize information from such a chaotic corpus, it struggles to find a single source of truth, leading to fabricated or incorrect responses.
2. Pattern Matching vs. Factual Understanding: Large Language Models are excellent at recognizing patterns in language and predicting the next most probable word or phrase. However, they don't possess genuine understanding or a built-in truth mechanism. If the patterns in their training data are ambiguous or contradictory, they might generate confident-sounding but incorrect answers.
3. Outdated Information: The world changes constantly. An Artificial Intelligence model trained years ago on static data will not have access to the most current information, leading it to "hallucinate" responses based on old facts.
4. Lack of Contextual Constraints: Without specific guardrails, Large Language Models can draw from their entire vast knowledge base, which may include irrelevant or misleading information for a particular query. This broad context can dilute accuracy.

The consequence of these factors is significant. On average, when integrating proprietary enterprise data with an Artificial Intelligence solution like a cloud-based Large Language Model, organizations often observe a hallucination rate of approximately one in every five user queries, equating to a 20 percent error rate. This level of inaccuracy is a major roadblock to enterprise-wide Artificial Intelligence adoption.

Introducing AirgapAI and Blockify: Your Foundation for Trusted Artificial Intelligence

Iternal Technologies developed AirgapAI as a fully isolated, 100 percent local Generative Artificial Intelligence (GenAI) chat assistant. It runs entirely on your device, such as an Artificial Intelligence Personal Computer (AI PC), ensuring unparalleled security by keeping all data within your premise. AirgapAI delivers superior accuracy at a fraction of the cost of cloud-based competitors.

At the heart of AirgapAI's unparalleled accuracy is Blockify, a patented data ingestion and optimization technology. Blockify is designed to eliminate the messiness of enterprise data, transforming it into a structured, highly accurate, and trustable corpus specifically optimized for Large Language Models.

Blockify Technology Deep Dive: From "Docs" to "Blocks"

Blockify is the ultimate data management solution for Large Language Models at scale. It allows you to create precise question and answer pairs, forming a "single source of truth" structure that empowers Large Language Models to answer your questions with unprecedented precision.

The Blockify Process: How Data is Transformed

The Blockify process ingests large, unstructured datasets—think thousands of sales documents, Request for Proposal (RFP) responses, compliance manuals, or clinical trial results—and condenses them into concise, modular units called "blocks." Each block is meticulously crafted to ensure maximum accuracy and utility for Artificial Intelligence.

Let's break down the components of an IdeaBlock (the core unit within Blockify):

1. Block Name: This is a clear, concise title for the content of the block, typically displayed in blue within the Blockify interface. It allows users to quickly identify the subject matter. (Image Note: Imagine a user interface showing a list of blue-colored titles, each representing a distinct IdeaBlock.)
2. Critical Question: This is the key query that a customer, employee, or system might ask related to the block's content. It acts as a highly focused prompt, guiding the Large Language Model to the most relevant information.
3. Trusted Answer: This is the distilled, accurate, and approved response to the critical question. It is crafted to avoid the pitfalls of outdated, redundant, or misleading data found in original source documents. Within the interface, these are often shown in light gray text. (Image Note: Visualize a section of the interface displaying a question in bold, italicized text, followed by its corresponding light gray, trusted answer.)

Ensuring Security and Data Lifecycle Management

Beyond just structuring data, Blockify incorporates robust security and data governance features essential for zero-trust environments:

• Rich Metadata Tagging: Each block is tagged with comprehensive metadata, including classification levels (e.g., Public, Internal, Confidential, Secret), permissions (who can access or use this data), and classification types. This ensures that sensitive information is only accessible to authorized users and models.
• Human-in-the-Loop Review: After the initial ingestion and block creation, these blocks are routed for a swift human review. This critical step allows subject matter experts to update, refine, or approve messaging, ensuring that outdated content (for example, from 2019) is flagged and prevented from influencing Artificial Intelligence responses. This human oversight is crucial for maintaining data quality and trust. (Image Note: Picture a workflow diagram showing documents entering Blockify, then being processed into blocks, followed by an arrow pointing to a human icon for review and approval.)

The Impact: Unprecedented Accuracy and Efficiency

The results of the Blockify process are transformative:

• Data Size Reduction: Blockify can reduce the original data size by as much as 97.5 percent (down to 2.5 percent of the original volume). This not only makes data more manageable but also significantly improves the efficiency of Large Language Models.
• Accuracy Improvement: Most remarkably, this process can improve the accuracy of your Large Language Models by an astonishing 7,800 percent, or 78 times! This virtually eliminates Artificial Intelligence hallucinations, providing highly reliable and trustworthy responses.

Workflow: Your Step-by-Step Guide to "Docs" to "Blocks" with Blockify

Let's walk through the process of using Blockify to ingest your documents and create an accurate dataset for AirgapAI.

1. Preparing Your Documents for Blockify

For the best results, it's recommended that customer data be curated into relevant categories, such as specific product lines, business units, or project types. This takes full advantage of Blockify's hierarchical metadata and taxonomy framework.

Blockify supports a wide array of file formats natively:

• Text files (.txt)
• HyperText Markup Language (HTML)
• Portable Document Format (.pdf)
• Microsoft Word documents (.docx)
• Microsoft PowerPoint presentations (.pptx)
• Graphic files (e.g., .jpg, .png)
• For video content, Blockify can extract still frames or transcribe audio as needed.

2. Demonstrating Blockify in Action (The User Interface)

While a live demonstration offers the best experience, here's how the workflow typically proceeds within the Blockify user interface:

1. Create a New Task: You'll begin by creating a new task within Blockify, for instance, naming it "AirgapAI Demo Data."
2. Upload Your Documents: Upload your source documents directly into Blockify. The system will then automatically begin to extract key information and propose initial blocks.
3. Reviewing IdeaBlocks:
   • Blue Block Names: As mentioned, these prominently identify the content topics for quick navigation.
   • Bold, Italicized Critical Questions: These highlight the precise queries that the Artificial Intelligence will be trained to answer.
   • Light Gray Trusted Answers: These represent the distilled, approved, and highly accurate responses synthesized by Blockify.
4. The Human Review Process: After the initial ingestion, these proposed blocks are sent for human review. This is where your subject matter experts ensure accuracy, update any outdated information, and approve the messaging. This step is critical to prevent any legacy or incorrect data from influencing your Artificial Intelligence.

Once reviewed and approved, these Blockified datasets are ready to be integrated into AirgapAI. Updates to these datasets can be easily pushed to local devices via standard image management applications like Microsoft Intune or similar solutions, ensuring your Artificial Intelligence always has the most current information.

How Blockify Powers AirgapAI for Accurate Artificial Intelligence

With your data Blockified, AirgapAI leverages this highly optimized corpus through a process called Retrieval-Augmented Generation (RAG).

1. AirgapAI Application Overview:

   • AirgapAI is a locally installed, chat-like application that runs open-source Large Language Models entirely on your client device (e.g., a Dell Precision workstation). This means no external network dependencies, ensuring complete data sovereignty and security.
   • It's distributed as an executable application that integrates seamlessly into standard Information Technology imaging workflows. Just like you'd open Microsoft Word or Excel, you open AirgapAI.
   • Users can choose from a suite of pre-quantized, open-source models (such as Llama, Mistral, DeepSeek) or even bring their own models.

2. Retrieval-Augmented Question and Answer with Blockify Datasets:

   • Within the AirgapAI application, you toggle your curated Blockify dataset "ON" in the sidebar. For example, if you have a dataset about Iternal Technologies extracted from sales materials, you select "Iternal Technologies Enterprise Portfolio Overview."
   • When you ask a question like "What is Iternal Technologies?" or "What is AirgapAI?", the Retrieval-Augmented Generation engine within AirgapAI performs a sophisticated search.
   • It identifies and ranks the top IdeaBlocks that best answer your question from your trusted, Blockified dataset.
   • The Large Language Model then synthesizes a coherent, trusted answer based only on the information contained within those highly accurate IdeaBlocks, critically showing citations to the source blocks. This constrained generation virtually eliminates hallucinations.

Example Query:

Imagine you've Blockified all your company's product documentation.

• Your Query: "What are the key features of the latest 'Quantum Leap' product line?"
• AirgapAI's Process: It searches your Blockified "Quantum Leap Product Documentation" dataset. It identifies IdeaBlocks covering specific features (e.g., "Feature A," "Feature B," "Feature C").
• AirgapAI's Response: A concise, accurate summary of those features, directly sourced from the trusted answers within your IdeaBlocks, with clear citations.

This direct linkage to curated, pre-approved information is what drives the 78 times improvement in accuracy.

Measuring Accuracy: Benchmarks Pre and Post Blockify

The dramatic reduction in Artificial Intelligence hallucinations isn't just a claim; it's a measurable outcome. Our internal evaluations demonstrate the power of Blockify.

The Benchmarking Process:

AirgapAI includes an in-app benchmarking suite. When you launch a model for the first time, AirgapAI offers to benchmark your hardware, measuring tokens per second and inference speed. While this focuses on performance, the qualitative difference in accuracy after Blockify is even more impactful.

Conducting Your Own Accuracy Test:

To truly appreciate the impact, consider a simple test:

1. Pre-Blockify (General Purpose Large Language Model): Start a chat in AirgapAI using a general-purpose Large Language Model (trained on the public internet) without any Blockified datasets enabled. Ask it a specific question about your company's proprietary information, for example, "What is our company's policy on remote work expenses?" You will likely receive a generic or even hallucinated response, as the model has no specific knowledge of your internal policies.
2. Post-Blockify (Curated Dataset Enabled): Now, enable your Blockified dataset that contains your company's Human Resources policies. Ask the exact same question: "What is our company's policy on remote work expenses?"
3. Observe the Difference: You will observe AirgapAI identifying and utilizing the relevant IdeaBlocks from your curated dataset to provide a precise, accurate, and trustworthy answer, often citing the source block.

This simple comparison will clearly illustrate the profound difference Blockify makes in eliminating hallucinations and delivering genuinely accurate Artificial Intelligence responses.

Benefits Beyond Accuracy: A Holistic Approach to Artificial Intelligence

While accuracy is paramount, AirgapAI and Blockify offer a suite of additional benefits that contribute to a holistic, secure, and efficient Artificial Intelligence experience:

🔒 Enhanced Security and Data Sovereignty

• 100% Local Operation: AirgapAI runs entirely on your device, with no network "in" or "out." This means your sensitive data never leaves your environment, mitigating risks of external network breaches and ensuring complete data sovereignty. This is ideal for high-security environments like government agencies, defense applications, and industries with strict compliance regulations.
• Zero-Trust Environment Support: Through Blockify's rich metadata, classification, and permissions tagging, AirgapAI fully supports zero-trust security models, ensuring that only authorized users and roles can access specific datasets.

💰 Significant Cost-Effectiveness

• Perpetual Licensing: AirgapAI is sold as a one-time perpetual license per device. You own the software, eliminating the burden of managing yet another subscription in your annual budget.
• Fraction of the Cost: AirgapAI runs at roughly one-tenth to one-fifteenth the cost of alternatives like Microsoft CoPilot, ChatGPT Enterprise, and other cloud solutions (targeting $2-$4 per user per month compared to standard $20-$30 or higher alternatives). This offers massive savings while driving a high Return on Investment.
• No Hidden Fees: Avoid ongoing subscription fees, hidden token charges, or overage bills common with cloud-based Artificial Intelligence providers.

🚀 Ease of Deployment and Use

• One-Click Installation: AirgapAI is delivered as an executable file that integrates straightforwardly into your standard Windows imaging process. Our deployment manual provides detailed instructions, and on average, users can be up and running in under 10 minutes.
• Seamless Integration: The application can be imaged as part of the standard Information Technology image process. Application updates and dataset updates can be pushed to the device using familiar image management solutions, just like any other enterprise software.
• No Prompt Engineering Expertise Required: AirgapAI is designed for the business user. With customizable user-based workflows, you don't need to be an expert in prompt engineering to get valuable outputs.

🌐 Uninterrupted Offline Access

• Productivity Anywhere: Stay productive with Artificial Intelligence-powered tools that work locally, even when you're offline or away from Wireless Fidelity (Wi-Fi). This is critical for field personnel, manufacturing facilities, remote cell tower engineers, or military personnel on missions where communication might be restricted.
• No Network Latency: Operate free from data center connection dependencies, ensuring peak performance without network latency.

👥 Role-Based Workflows and Entourage Mode

• Quick Start Workflows: AirgapAI includes Quick Start workflows tailored for different roles (e.g., procurement, legal, engineering). These pre-configured prompts automatically select relevant curated (Blockified) datasets, streamlining tasks and ensuring context-specific accuracy.
• Entourage Mode: A unique feature allowing users to interact with multiple Artificial Intelligence personas simultaneously. For example, when preparing a complex proposal, your marketing, legal, and technical support personas can weigh in, lending different perspectives from their respective datasets. In a defense scenario, a Central Intelligence Agency (CIA) analyst persona and a military tactician persona can provide distinct answers to the same question from their specialized knowledge bases, offering multi-perspective views on complex issues.

AirgapAI Installation and Initial Setup

Getting started with AirgapAI is designed to be simple and user-friendly, even if you're new to Artificial Intelligence applications. Here's a brief overview; for complete, granular detail, always refer to the full AirgapAI Installation Guide.

System Requirements and Prerequisites

AirgapAI is optimized for modern Personal Computers (PCs) and Artificial Intelligence Personal Computers (AI PCs).

Permissions: You will need security permissions to install applications on your device.

Downloading and Installing the Application

1. Obtain Installer: Acquire the latest ZIP archive from the internal or cloud link provided by your Information Technology department. Save it to your Downloads folder or another writable location.
2. Extract Files: Right-click the ZIP file (e.g., AirgapAI-v1.0.2-Install.zip) and select "Extract All..." Choose a destination (the default is usually a new folder in Downloads) and click "Extract."
3. Run Setup: Open the extracted folder and double-click AirgapAI Chat Setup.exe.
4. Follow Wizard: Proceed through the installer wizard: accept the license agreement, choose to create a Desktop Shortcut, click "Install," and then "Finish." If prompted by your operating system's security (e.g., SmartScreen), choose "Allow" or "Run anyway."

First-Launch Onboarding Wizard

Upon the first launch of AirgapAI Chat, if no existing models are found, an Onboarding flow will begin:

1. Profile & Chat Style: Click "Start Onboarding," enter a display name (default: "You"), and pick your preferred Chat Style (e.g., Iternal Professional, Casual, Dark Mode). Click "Next."
2. Uploading the Core Large Language Model: Expand the "Available Models" drop-down (it will be empty initially). Click "Upload Model," browse to the /models/ subfolder within your extracted installer folder. Select a model suited to your hardware (e.g., Llama-1B for Integrated Graphics Processing Unit or low-power, Llama-3B for newer Integrated Graphics Processing Unit or dedicated Graphics Processing Unit). Click "Save." This takes approximately 30 seconds.
   • Note for Administrators: You can manage Large Language Models in %appdata%\IternalModelRepo.
3. Uploading an Embeddings Model: Still on the onboarding page, click "Upload Embeddings Model." Open /models/ and select Jina-Embeddings.zip. Click "Save" (approximately 30 seconds).
   • Note for Administrators: Embeddings Models are managed in %appdata%\IternalModelRepo.
4. Adding Sample or Custom Datasets: Click "Upload Dataset," navigate to the /datasets/ subfolder, and select a sample dataset (e.g., CIA_World_Factbook_US.jsonl). Click "Save."
   • Tip: While you can upload Word, PDF, or TXT directly, converting larger corpora to Blockify format yields approximately 78 times accuracy gain. Local, on-device Blockify will be available in Quarter 3, 2025.
   • Note for Administrators: Datasets are managed in %appdata%\airgap-ai-chat\CorpusRepo.
5. Finish Onboarding: Verify that all three items (Core Large Language Model, Embeddings Model, Dataset) are added, then click "Continue." AirgapAI Chat will now boot with your selections.

Initial Model Benchmarking

On the first model launch, AirgapAI Chat offers to benchmark your hardware. It's recommended to "Run Benchmark" (takes approximately 2 minutes) to measure tokens per second and inference speed. This helps optimize context-size limits.

Conclusion: Trust and Scale Your Artificial Intelligence

The era of unreliable Artificial Intelligence, plagued by hallucinations and data security concerns, is over. With Iternal Technologies' AirgapAI and its revolutionary Blockify technology, you can achieve unprecedented levels of accuracy, security, and cost-efficiency in your Artificial Intelligence initiatives.

Empower your Chief Information Security Officer, compliance team, operations, and Information Technology departments with an Artificial Intelligence solution they can truly trust. Turn skepticism into adoption, and transform your data into a powerful, hallucination-free asset.

Take the Next Step Towards Trusted Artificial Intelligence

Ready to experience the power of 78 times more accurate Artificial Intelligence?

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