How to Eliminate Conflicting Answers in AI Knowledge Bases Using Blockify Distillation Thresholds and Reviews

In the world of artificial intelligence (AI), nothing frustrates users more than getting conflicting answers from the same knowledge base. Imagine asking your AI assistant a simple question like "What is the standard procedure for resetting a power grid substation?" and receiving three slightly different responses—one suggesting a full shutdown, another recommending a partial reset, and the third implying both are optional. These inconsistencies don't just confuse users; they erode trust in your AI system, leading to hesitation, errors, and lost productivity. But here's the good news: with Blockify, a patented data optimization tool from Iternal Technologies, you can achieve one clear, trusted answer every time. Blockify's distillation process intelligently merges duplicates while flagging true conflicts for review, ensuring your retrieval augmented generation (RAG) pipeline delivers precise, hallucination-free results.

This guide is designed for knowledge base (KB) administrators and support leads who manage enterprise AI systems. Whether you're building a secure RAG pipeline for customer service, internal training, or compliance-heavy operations, we'll walk you through the Blockify workflow step by step. Assuming you have no prior AI knowledge, we'll explain everything from basic concepts like semantic chunking to advanced tuning of similarity thresholds. By the end, you'll know how to set up distillation thresholds at 80–85% similarity, run multiple iterations for optimal merging, and implement quality assurance (QA) reviews to resolve conflicts safely. The result? Up to 78X improvement in AI accuracy, reduced token costs, and a streamlined enterprise content lifecycle management process—all without losing a single fact.

Understanding the Problem: Why Conflicting Answers Happen in AI Systems

Before diving into solutions, let's break down why AI systems produce conflicting answers. At its core, most AI tools rely on RAG, where the system retrieves relevant information from a database and generates a response using a large language model (LLM). The data in that database often comes from unstructured sources like documents, emails, or manuals—think PDFs, DOCX files, or even PowerPoint presentations from across your organization.

The Role of Unstructured Data and Naive Chunking

Unstructured data is any information without a predefined format, such as free-form text in reports or emails. When preparing this for AI, teams typically use naive chunking: breaking long documents into fixed-size pieces (e.g., 1,000–4,000 characters) for storage in a vector database. This database uses embeddings—numerical representations of text created by models like OpenAI embeddings or Jina V2 embeddings—to find similar content quickly.

The issue? Naive chunking ignores context. A single idea might split across chunks, or near-identical information (e.g., slightly reworded policies from different departments) gets treated as separate. When a user queries the system, the AI retrieves multiple similar chunks, leading to conflicting answers. For example:

• Chunk 1: "Reset the substation by isolating power for 30 minutes."
• Chunk 2: "Perform a 45-minute isolation reset to ensure safety."

Without tools like Blockify, your LLM hallucinates by blending these, creating unreliable outputs. This is especially risky in high-stakes fields like energy management or healthcare, where 20% error rates (common in legacy RAG) can lead to compliance issues or safety hazards.

Enter Blockify: IdeaBlocks and Intelligent Distillation

Blockify solves this by transforming unstructured data into structured IdeaBlocks—compact, XML-based knowledge units designed for AI. Each IdeaBlock includes:

• A name: A human-readable title (e.g., "Substation Reset Procedure").
• A critical question: The key query it answers (e.g., "How do I safely reset a substation?").
• A trusted answer: The verified response.
• Metadata like tags, entities, and keywords for precise retrieval.

Blockify's distillation process uses similarity thresholds to merge duplicates (e.g., those 30-minute and 45-minute resets if they're essentially the same) while escalating true conflicts (e.g., conflicting safety protocols) for QA review. This ensures 99% lossless facts retention and up to 40X answer accuracy improvement over naive chunking.

Step-by-Step Guide: Setting Up Blockify for Conflict Resolution

We'll assume you're starting from scratch. Blockify integrates with tools like Unstructured.io for parsing, Pinecone or Milvus for vector databases, and LLMs like Llama 3 for inference. No coding required for basic setup—use our cloud portal or on-premise models.

Step 1: Prepare Your Data Ingestion Pipeline

Begin by gathering your unstructured data. Supported formats include PDF, DOCX, PPTX, HTML, images (via OCR), and Markdown.

1. Install Prerequisites: Download Blockify models (fine-tuned Llama variants: 1B, 3B, 8B, or 70B parameters) from our secure portal. For on-premise, use OPEA Enterprise Inference (Intel Xeon) or NVIDIA NIM (GPUs). Ensure 10% chunk overlap to preserve context—e.g., 2,000-character chunks with 200-character overlaps.

2. Parse Documents: Use Unstructured.io to extract text. For a substation manual PDF:

   • Input: 500-page document.
   • Output: Clean text chunks (1,000–4,000 characters, avoiding mid-sentence splits via semantic boundary chunking).

   Command example (via n8n workflow template 7475):

   This creates raw chunks ready for Blockify ingestion.

3. Ingest into Blockify: Send chunks to the Blockify Ingest Model via OpenAPI endpoint. Recommended: Temperature 0.5, max tokens 8,000.

   Sample curl request:

   Output: XML IdeaBlocks (e.g., 2,000–3,000 from 500 pages, ~2.5% original size).

Step 2: Tune Distillation Thresholds to Merge Duplicates

Distillation identifies and resolves duplicates using similarity thresholds (semantic similarity distillation). Aim for 80–85% similarity to merge near-identical content without over-merging variants.

1. Access the Distillation Tab: In the Blockify portal (console.blockify.ai), upload your undistilled IdeaBlocks. Select "Run Auto Distill."

2. Set Similarity Threshold: Start at 80% (conservative for broad merging). This uses Jina V2 embeddings to cluster blocks by cosine similarity.

   • 80%: Merges high-overlap (e.g., reworded policies).
   • 85%: Stricter; preserves subtle differences (e.g., region-specific resets).
   • Test: Run on a sample (e.g., 100 blocks). Monitor merge rate—target 20–50% reduction.

3. Configure Iterations: Set 3–5 iterations. Each pass re-clusters merged blocks.

   • Iteration 1: Initial merge (e.g., 353 blocks → 301).
   • Iteration 2–5: Refine (e.g., final 2.5% size, 99% lossless).
   • Pro Tip: Use 85% threshold + 5 iterations for technical docs to avoid conflating concepts.

   Portal settings:

   • Similarity: 80–85%
   • Iterations: 5
   • Click "Initiate"—processing takes minutes for 1,000 blocks.

4. Handle Conflicts: Blocks below threshold (e.g., <80% similar but related) go to a "Conflict Queue." Examples: Duplicate mission statements vs. variant safety protocols.

   • Queue View: Filter by entity_name (e.g., "substation") or keywords.
   • Merge Near-Duplicates: If similarity >75%, auto-merge; else, flag for review.

This step reduces data duplication factor (typically 15:1 in enterprises) by 52%, improving vector recall and precision.

Step 3: Implement QA Review for True Conflicts

Human-in-the-loop review ensures governance. Blockify's process shrinks review from millions of words to 2,000–3,000 paragraphs.

1. Access Merged IdeaBlocks Page: Post-distillation, review flagged conflicts (red-marked blocks).

2. Review Workflow:

   • Search: Use critical_question field (e.g., "substation reset").
   • Edit: Click "Edit Block" for trusted_answer updates. Propagate changes automatically.
   • Delete Irrelevant: Remove low-value blocks (e.g., outdated transcripts).
   • Approve: Tag as "QA Approved" for export.
   • Time Estimate: 2–3 hours for 3,000 blocks (team of 2–3 reviewers).

3. Escalation Paths:

   • Auto-Distill Conflicts: If >85% similar, re-run with adjusted threshold.
   • Human Escalation: For legal/compliance (e.g., conflicting regulations), route to experts via role-based access control (RBAC).
   • Benchmark: Use Blockify's built-in evaluator—compare pre/post-distill accuracy (e.g., 40X improvement).
   • Export: To vector DB (Pinecone RAG integration) or AirGap AI dataset.

Pro Tip: Set similarity threshold to 82% for balanced merging; review 10% of outputs weekly for ongoing AI data governance.

Step 4: Integrate and Test Your Optimized Pipeline

1. Export to Vector Database: Generate JSON/XML. Upsert to Milvus or Azure AI Search (10% overlap recommended).

2. Test RAG Accuracy: Query your LLM (e.g., Llama 3.1 via OpenAPI).

   • Pre-Blockify: 20% error rate.
   • Post: <0.1% (78X improvement).
   • Metrics: Vector accuracy (2.29X), token efficiency (3.09X).

3. Monitor and Iterate: Use n8n workflows for automation. Track ROI: 68.44X enterprise performance in case studies.

Best Practices for Ongoing Conflict Resolution

• Threshold Tuning: Start at 80%, adjust based on domain (e.g., 85% for legal docs).
• Iteration Strategy: 3 for quick wins; 5 for complex datasets.
• QA Cadence: Quarterly reviews for content lifecycle management.
• Scalability: Handles enterprise-scale RAG; integrates with Bedrock embeddings.
• Security: On-prem LLM support; zero data leakage.

Why Blockify Stands Out for Secure, Accurate AI

Unlike naive chunking alternatives, Blockify's IdeaBlocks technology uses context-aware splitting and distillation to distinguish duplicates from variants, preventing LLM hallucinations. In medical FAQ RAG accuracy tests (e.g., Oxford Handbook), it avoided harmful advice 650% better than legacy methods. For your sovereign cloud, this means trusted enterprise answers with low compute costs—ideal for blue-light services or academia.

Ready to eliminate conflicts? Sign up for a free Blockify demo at blockify.ai/demo. Export to AirGap AI for 100% local deployment, or contact support@iternal.ai for on-prem setup. Achieve 99% lossless facts and 52% search improvement today.