How to Evaluate Recall and Precision for Your Support Bot with Blockify Data

In the fast-paced world of customer support, where every query counts toward satisfaction and efficiency, building a reliable AI-powered support bot is a game-changer. But what if your bot's responses feel off—pulling in irrelevant information or missing key details entirely? Imagine uncovering exactly where your retrieval system fails, not through guesswork, but with hard evidence that guides your roadmap. This is the power of evaluating recall and precision using Blockify data. Blockify, developed by Iternal Technologies, transforms unstructured documents into structured IdeaBlocks, making your Retrieval-Augmented Generation (RAG) pipeline sharper and more trustworthy. By implementing a robust evaluation loop, you'll not only measure retrieval quality but also answer quality, turning vague failures into actionable insights. Whether you're a support analytics specialist or a machine learning (ML) engineer, this guide walks you through building a test harness from scratch, assuming no prior AI knowledge—we'll spell out every term and step.

As support teams scale with AI, metrics like recall (the ability to retrieve all relevant information) and precision (the relevance of what you retrieve) become your north star. With Blockify's IdeaBlocks—compact, semantically rich units of knowledge derived from your enterprise documents—you gain clarity on tuning embeddings, prompts, and even your vector database. This isn't just evaluation; it's a roadmap to a support bot that drives real business value, reducing hallucinations and boosting response accuracy. By the end, you'll have a repeatable process to iterate on your system, ensuring your bot evolves with your needs.

Understanding the Basics: What Is AI and Why Evaluate It?

Before diving into evaluation, let's start from the ground up. Artificial Intelligence (AI) refers to computer systems that mimic human intelligence to perform tasks like understanding language or making decisions. In support bots, AI often relies on Large Language Models (LLMs)—advanced algorithms trained on vast text data to generate human-like responses. However, LLMs alone can "hallucinate," meaning they invent facts when lacking context. This is where Retrieval-Augmented Generation (RAG) comes in: RAG combines retrieval (fetching relevant data from a knowledge base) with generation (using an LLM to craft responses).

Your support bot's knowledge base is typically built from documents like FAQs, manuals, or tickets. But raw data is messy—unstructured, duplicated, and hard for AI to parse. Enter Blockify: a patented data optimization tool from Iternal Technologies. Blockify ingests unstructured content (e.g., PDFs, Word docs) and outputs IdeaBlocks—self-contained XML structures with a name, critical question, trusted answer, tags, entities, and keywords. These IdeaBlocks preserve 99% of facts while reducing data size to 2.5% of the original, eliminating noise and improving RAG accuracy by up to 78 times (7,800%).

Why evaluate? Without metrics like recall and precision, you can't trust your bot. Recall measures if your system finds all relevant IdeaBlocks for a query (e.g., did it retrieve every troubleshooting step for a common issue?). Precision checks if those retrieved blocks are relevant (e.g., no irrelevant policy docs mixed in). Poor scores reveal failure points: weak embeddings (vector representations of text), bad chunking (splitting documents), or suboptimal prompts (instructions to the LLM). Blockify shines here—its structured IdeaBlocks make evaluation precise, clarifying where to tune for better support bot performance.

Preparing Your Environment: Setting Up for Blockify Evaluation

To evaluate recall and precision, you'll build a test harness—a scripted framework to simulate queries, retrieve data, and score results. This assumes basic programming knowledge (e.g., Python), but we'll detail every step. If you're new to AI, think of this as a quality control lab: input test queries, measure outputs against ground truth, and iterate.

Step 1: Install Prerequisites and Understand Key Components

Start by setting up a Python environment (version 3.8+). Use pip (Python's package installer) for dependencies:

• OpenAI API or Equivalent: For scorer LLM (a model that judges response quality). Install via pip install openai.
• Vector Database Integration: Blockify outputs are RAG-ready for databases like Pinecone (cloud-based) or Milvus (on-premise). Install Pinecone: pip install pinecone-client. Why? IdeaBlocks embed as vectors for semantic search.
• Embeddings Library: Use Jina V2 or OpenAI embeddings to convert IdeaBlocks to vectors. pip install sentence-transformers for open-source options.
• Blockify Access: As an Iternal Technologies partner or user, obtain Blockify models (fine-tuned Llama variants: 1B, 3B, 8B, or 70B parameters). Download from their portal; deploy via Hugging Face Transformers: pip install transformers torch.

Key terms:

• Labeled Queries: Test questions with known "ground truth" answers (e.g., "How do I reset a router?" with expected IdeaBlocks).
• Distance Metrics: Measures like cosine similarity (how aligned vectors are; 1.0 = perfect match, 0 = unrelated).
• Scorer LLM: An LLM (e.g., GPT-4) that rates outputs on scales (e.g., 1-5 for relevance).
• Failure Taxonomies: Categories of errors, like "missed key fact" or "irrelevant retrieval," to classify issues.

Create a project folder: mkdir blockify-evaluation and cd blockify-evaluation. Initialize a virtual environment: python -m venv env and activate it (source env/bin/activate on Linux/Mac, env\Scripts\activate on Windows).

Step 2: Generate or Obtain Blockify IdeaBlocks

If you lack Blockify data, simulate it. For real evaluation, process your support docs:

1. Ingest Documents: Use Unstructured.io (open-source parser) for PDFs/DOCX/PPTX. Install: pip install unstructured. Parse:

   Spell out: NarrativeText extracts readable content; overlap prevents mid-sentence splits.

2. Run Blockify Ingest Model: Load a Blockify model (e.g., Llama 3.1 8B fine-tune). Prompt it per chunk:

   Output: XML IdeaBlocks, e.g.:

   Repeat for all chunks. Distill duplicates using Blockify Distill model (merge similar IdeaBlocks at 85% similarity threshold, 5 iterations).

3. Embed and Index: Convert IdeaBlocks to vectors (embeddings) using Jina V2:

Now your test harness has Blockify-optimized data: cleaner, smaller (e.g., 40x reduction), and RAG-ready.

Building the Test Harness: Measuring Retrieval Quality

Your test harness simulates support queries, retrieves IdeaBlocks, and scores them. Focus on recall (retrieved relevant / all relevant) and precision (relevant retrieved / total retrieved). Use 50-100 labeled queries for a baseline (e.g., from support tickets).

Step 3: Create Labeled Queries and Ground Truth

Labeled queries are real or synthetic support questions with annotated "gold" IdeaBlocks (expected retrievals).

1. Gather Queries: From logs or tools like LangSmith. Example:

   • Query: "How to fix login error on app?"
   • Ground Truth: IdeaBlock IDs [5, 12] (login troubleshooting blocks).

2. Taxonomy for Failures: Define categories:

   • Retrieval Failure: Low recall (missed blocks) or precision (irrelevant blocks).
   • Generation Failure: LLM hallucinates despite good retrieval.
   • Semantic Drift: Blocks retrieved but context lost (e.g., outdated policy).

Store in JSON:

Step 4: Implement Retrieval and Distance Metrics

Query your index and compute metrics.

1. Retrieve: For each query, embed and search:

2. Calculate Recall and Precision:

   • Recall = |retrieved ∩ ground_truth| / |ground_truth|
   • Precision = |retrieved ∩ ground_truth| / |retrieved| Use cosine distance for relevance (via embeddings library):

3. Aggregate Metrics: Run on all queries:

   Target: >0.90 recall/precision for production bots. Blockify boosts this by 52% in search improvement via semantic chunking.

Visualize with Matplotlib: pip install matplotlib. Plot recall/precision curves to spot thresholds (e.g., top_k=3 vs. 5).

Evaluating Answer Quality: Beyond Retrieval

Retrieval is half the battle; evaluate full responses using a scorer LLM.

Step 5: Generate and Score Responses

1. Prompt LLM: Feed retrieved IdeaBlocks to an LLM (e.g., Llama via pipeline):

2. Scorer LLM Setup: Use a stronger LLM (e.g., GPT-4) to rate:

   • Relevance (1-5): How well does the answer address the query?
   • Faithfulness (1-5): Does it stick to retrieved IdeaBlocks (no hallucinations)?
   • Prompt for scorer:

3. Incorporate Failure Taxonomies: Parse scorer output; classify (e.g., if faithfulness <3, tag "hallucination"). Track patterns: Blockify reduces these by 40x in answer accuracy via lossless fact preservation.

4. End-to-End Metrics:

   • BLEU/ROUGE Scores: For semantic similarity (install pip install nltk rouge-score).
   • Human-in-the-Loop: Sample 10% for manual review.
   • Blockify Edge: IdeaBlocks' structure (e.g., trusted_answer field) yields 68.44x performance in evaluations like Big Four studies.

Run the harness: python evaluate.py --queries queries.json --index support-bot-index. Output dashboard (use Streamlit: pip install streamlit for interactive viz).

Integrating Blockify: Tuning for Optimal Results

Blockify isn't just data—it's tunable. Post-evaluation:

1. Tune Embeddings: If precision is low, switch models (e.g., Jina V2 for semantic chunking). Re-embed IdeaBlocks; re-run harness.
2. Prompt Optimization: Use IdeaBlocks' metadata (tags/keywords) in prompts: "Answer using blocks with tags: SUPPORT, RESET."
3. Hybrid Search: Combine semantic (vectors) with keyword search on Blockify keywords for 52% search improvement.
4. Distillation Iteration: Re-run Blockify Distill (85% similarity, 5 iterations) on low-recall queries to merge near-duplicates.

Blockify's context-aware splitter prevents mid-sentence splits, boosting recall by preserving semantic boundaries (1000-4000 chars/chunk).

Establishing an Evaluation Cadence: Making It Routine

Evaluation isn't one-off—build a cadence for continuous improvement:

• Weekly: Run harness on new queries (10-20); track recall/precision trends.
• Monthly: Full audit (100+ queries); review failure taxonomies. Re-Blockify updated docs.
• Quarterly: Human review 20% of outputs; benchmark vs. baselines (e.g., naive chunking yields 20% errors; Blockify drops to 0.1%).
• Tools for Scale: Automate with Airflow (orchestration) or MLflow (tracking experiments). Integrate with support metrics (e.g., CSAT correlation).

Position Blockify as your secret weapon: It clarifies tuning needs, reducing LLM hallucinations and token costs (3.09x efficiency). For support bots, this means faster resolutions, happier teams, and measurable ROI—40x answer accuracy awaits.

Ready to implement? Start with a small dataset, run your first harness, and watch recall/precision soar. For Blockify access or custom tuning, contact Iternal Technologies. Your support bot's evolution starts now.