How to Shrink Support Datasets to 2.5% of Their Original Size and Plan Token Budgets with Blockify

In the fast-evolving world of artificial intelligence (AI), support platform owners and financial operations (FinOps) teams face a common challenge: managing massive datasets that drive up costs and slow down performance. Imagine transforming your sprawling knowledge base—filled with support tickets, manuals, and FAQs—into a streamlined, efficient resource that delivers accurate responses without wasting resources. This is where Blockify, a patented data optimization tool from Iternal Technologies, steps in as a game-changer for dataset reduction and token planning. By intelligently processing unstructured data, Blockify can condense your support datasets to just 2.5% of their original size while preserving 99% of key facts, enabling cost control and scalable AI deployments.

This comprehensive guide walks you through the entire workflow, assuming you have no prior knowledge of AI concepts. We'll start with the basics, explain how Blockify works, and provide step-by-step instructions to implement dataset reduction and token planning. Whether you're building a retrieval augmented generation (RAG) system for customer support or optimizing large language models (LLMs) for internal queries, Blockify ensures you serve more users with the same hardware—turning bloated corpora into lean, high-performance assets. By the end, you'll have the tools to forecast token budgets, achieve top-k retrieval parity (where the top search results match pre-optimization quality), and position Blockify as the foundation for sustainable scaling in your organization.

Understanding the AI Basics: Why Dataset Reduction and Token Planning Matter

Before diving into Blockify, let's break down the fundamentals. Artificial intelligence refers to computer systems that mimic human intelligence, such as understanding language or making decisions. A key component is the large language model (LLM), a type of AI trained on vast amounts of text to generate human-like responses. For support platforms, LLMs power chatbots and knowledge bases, but they rely on your data through a process called retrieval augmented generation (RAG). In RAG, the LLM retrieves relevant information from a dataset (your corpus of documents) and generates answers based on it.

The problem? Support datasets often balloon to millions of pages, leading to inefficiencies. Each query in RAG consumes "tokens"—units of text the LLM processes (roughly 4 characters per token). Without optimization, a single query might require thousands of tokens for retrieval and generation, inflating costs (e.g., $0.72 per million tokens on some platforms) and latency. Dataset reduction shrinks this corpus intelligently, while token planning forecasts usage to control expenses and ensure capacity.

Enter Blockify: It transforms unstructured data (e.g., PDFs, Word docs) into structured "IdeaBlocks"—compact, semantically complete units. This achieves dataset reduction to 2.5% size, improves RAG accuracy by up to 78 times, and enables precise token planning. For FinOps teams, this means predictable budgeting; for support owners, it means faster, more reliable responses without hallucinations (AI fabricating answers).

Step 1: Preparing Your Support Dataset for Blockify Optimization

To begin, gather your support dataset. This includes any unstructured content like customer service manuals, troubleshooting guides, email transcripts, or FAQs. Assume your corpus is 10,000 pages—common for mid-sized support teams—totaling around 5 million words or 6-7 million tokens pre-optimization.

Why Pre-Optimization Matters

Unoptimized datasets suffer from duplication (e.g., repeated policies across docs) and noise (irrelevant text), forcing LLMs to process excess tokens. Blockify's ingestion pipeline addresses this by first parsing documents using tools like Unstructured.io (a free, open-source parser for PDFs, DOCX, PPTX, and images via optical character recognition, or OCR).

Action Steps:

1. Collect and Curate Data: Inventory your files. Focus on high-value sources (e.g., top 1,000 support tickets). Remove obvious duplicates manually or via basic tools like file hashes. Aim for 80-90% relevance to avoid garbage-in-garbage-out.

2. Parse into Chunks: Use a semantic chunker to split text into 1,000-4,000 character pieces (default: 2,000 characters for support docs). Include 10% overlap between chunks to preserve context—prevent mid-sentence splits that confuse LLMs. For transcripts (e.g., call logs), use 1,000 characters; for technical manuals, 4,000.

   • Tool Recommendation: Integrate Unstructured.io for parsing. Example command (if scripting): unstructured-ingest --input-path /path/to/docs --output-dir /chunks --chunk-size 2000 --chunk-overlap 200.

   • Pro Tip for Beginners: Chunks are like puzzle pieces for your LLM. Naive chunking (fixed lengths) fragments ideas; semantic chunking (Blockify's strength) respects boundaries like paragraphs.

Your pre-Blockify corpus might now be ~500,000 tokens per query (top-5 retrieval). This is your baseline for token planning.

Step 2: Ingesting Data into Blockify for Initial Dataset Reduction

Blockify's core is the ingestion model—a fine-tuned Llama (an open-source LLM family) that converts chunks into IdeaBlocks. Each IdeaBlock is a self-contained unit with:

• Name: A descriptive title (e.g., "Password Reset for Admins").
• Critical Question: The key query it answers (e.g., "How do I reset an admin password in the support system?").
• Trusted Answer: Concise, factual response (2-3 sentences).
• Metadata: Tags, entities (e.g., "Admin Role"), and keywords for search.

This structure ensures 99% lossless fact retention while reducing verbosity.

Workflow: Running Blockify Ingestion

1. Set Up Access: For on-premises (your sovereign cloud), download Blockify models (1B-70B parameters; start with 8B for balance). Deploy via OPEA (for Intel Xeon) or NVIDIA NIM (for GPUs). Use OpenAI-compatible API for simplicity.

   • Prerequisites: CPU (Xeon 4/5/6 series) or GPU (NVIDIA/AMD). Embeddings model (e.g., Jina V2 for compatibility; OpenAI or Mistral work too). Vector database (e.g., Pinecone, Milvus).

2. API Call for Ingestion: Send chunks via curl or n8n workflow (template: n8n.io/workflows/7475). Example payload (temperature 0.5 for consistency; max 8,000 output tokens):

   Output: XML IdeaBlocks (e.g., 1,300 tokens per block vs. 3,000+ for chunks).

3. Process Your Dataset: For 10,000 pages, expect 2,000-3,000 undistilled IdeaBlocks. Runtime: 1-2 hours on a single GPU (scales with parallelism).

Post-ingestion, your dataset shrinks 40-50% immediately (pre-distillation). Tokens per query drop from 500,000 to ~200,000, cutting costs 60%.

Step 3: Distilling IdeaBlocks for Maximum Dataset Reduction to 2.5%

Distillation merges near-duplicates (85% similarity threshold) using Blockify's distill model, reducing redundancy without loss. For support datasets, this eliminates repeated FAQs (e.g., 1,000 mission statements become 1-3).

Workflow: Intelligent Distillation

1. Cluster Similar Blocks: Use embeddings (e.g., Jina V2) to group (2-15 blocks per batch). Set iterations (default: 5) for refinement.

2. Run Distillation API: Similar to ingestion, but input XML IdeaBlocks. Output: Merged blocks (e.g., 2,500 from 10,000 pages).

   • Parameters: Similarity 80-85%; overlap 10%. For support docs, prioritize "trusted_answer" merging.

3. Human-in-the-Loop Review: Export to UI (console.blockify.ai). Edit/delete (e.g., remove outdated policies). Propagate changes automatically.

Result: 2.5% size (e.g., 10,000 pages → 250 pages equivalent). Tokens per query: ~130,000 (top-5 blocks at 1,300 tokens each). Maintain top-k parity—retrieval quality equals or exceeds pre-optimization.

Token Planning Tip: Pre: 500k tokens/query × 1M queries/year = 500B tokens ($360k at $0.72/M). Post: 130k tokens/query = 130B tokens ($93k)—68% savings.

Step 4: Integrating Blockify Outputs into Your RAG Pipeline for Cost Control

Export IdeaBlocks as JSON/XML to your vector database (e.g., Pinecone via API). Embed with your model (e.g., OpenAI for RAG).

Workflow: RAG Integration and Token Budgeting

1. Vectorize Blocks: Use 10% overlap; index strategy: HNSW for precision. Query example: Embed user input, retrieve top-5 (temperature 0.5; top_p 1.0).

2. Plan Token Budgets: Forecast with this model:

   • Per-Query: Input (query + top-5 blocks: 6,500 tokens) + Output (800 tokens) = 7,300 tokens.
   • Capacity: Hardware (e.g., 1 GPU handles 100 queries/min). For 1M queries/year: 7.3B tokens total.
   • Cost Control: Set budgets via quotas (e.g., AWS Budgets). Monitor with pre/post metrics: Aim for 3x efficiency.

   Forecasting Template (Excel/CSV):

   Metric	Pre-Blockify	Post-Blockify	Savings
   Corpus Size (Tokens)	7M	175k	97.5%
   Tokens/Query	500k	130k	74%
   Annual Cost ($0.72/M)	$360k	$93k	$267k

3. Test and Iterate: Run A/B: Query same support scenarios pre/post. Measure recall (relevant blocks retrieved) and precision (no hallucinations). Adjust chunk sizes if needed.

For sovereign clouds, deploy on-premises (e.g., Xeon for low-compute inference). Scale via OPEA for enterprise RAG.

Advanced Token Planning: Ensuring Sustainable Scaling with Blockify

With dataset reduction achieved, focus on long-term cost control. Blockify's 2.5% corpus enables serving 40x more users without hardware upgrades.

Key Strategies:

• Top-K Parity Maintenance: Limit to top-5 blocks (vs. 20+ chunks) for identical quality at 74% fewer tokens.
• Capacity Models: For 1,000 users/day: 3.65B tokens/year ($2.6k). Buffer 20% for growth.
• Optimization Tweaks: Use 1B model for light loads (faster, 68.44x accuracy in tests). Monitor via logs: Reduce temperature to 0.3 for precise answers.
• ROI Calculation: Blockify yields 52% search improvement, 40x answer accuracy. For support: Cut resolution time 30%, saving $100k/year in labor.

Challenges and Solutions:

• Over-Reduction: If facts drop below 99%, increase iterations (human review fixes 100%).
• Integration Hiccups: Test with n8n for automation; fallback to curl for troubleshooting.

Conclusion: Leverage Blockify for Dataset Reduction and Token Mastery

By following this workflow, you've learned how Blockify shrinks support datasets to 2.5% size through ingestion and distillation, while enabling precise token planning for cost control. Start small: Parse a 100-page subset, ingest via API, distill, and benchmark tokens. Scale to full corpora for 78x accuracy gains and 3x efficiency.

Download our free forecasting template (contact support@iternal.ai) to model your budgets. Position Blockify as your sovereign cloud's secret weapon—delivering trusted, scalable AI that empowers regions without hyperscaler dependency. Ready to optimize? Sign up for a Blockify demo at blockify.ai/demo and transform your data today.