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Why Standard RAG Text Splitters Hallucinate on Pricing Tables — And How Table-Aware Breadcrumb AST Chunking + Numeric Grounding Verification Fixes It

#saas #ai #rag #typescript

If you feed enterprise pricing pages, SOC 2 security questionnaires, or financial PDFs into a standard fixed-token RAG splitter (RecursiveCharacterTextSplitter with chunkSize: 512), your RAG pipeline will confidently hallucinate numbers.

Why? Because naive character and token splitters slice Markdown and HTML tables mid-row—separating row 35 (| Enterprise | $499/mo | $0.08/1k |) from its table header (| Tier | Base Price | Overage |) and stripping the parent section heading (## 2026 Vector Database Pricing).

1. Table-Aware AST Chunking with Header & Ancestor Breadcrumb Re-Injection

To preserve 100% numeric retrieval accuracy across multi-page tables, your chunker must parse the document AST, track the active heading hierarchy ([Context: H1 > H2 > H3]), and re-inject the Markdown table header and alignment row into every split table chunk:

// Teaser Excerpt: Table-Aware AST Chunker + Deterministic Numeric Verifier
// (Full AST parser + currency/percentage grounding verifier + SOC 2 API key kit below)
const breadcrumb = `[Context: ${headingStack.filter(Boolean).join(" > ")}]`;
const chunkWithHeaders = `${breadcrumb}\n${tableHeaderRow}\n${tableSeparatorRow}\n${sliceRows.join("\n")}`;

// Post-Generation Deterministic Numeric & Currency Hallucination Guard
const ungroundedNumbers = extractNumbers(llmAnswer).filter(n => !sourceChunkNumbers.has(n));
if (ungroundedNumbers.length > 0) return { verified: false, blockedNumbers: ungroundedNumbers };
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Benchmark Results Across 200 Financial & SaaS Pricing Queries

Chunking & Verification Strategy Table Row Retrieval Recall Uncaught Numeric Hallucination Rate
Naive 512-Token Character Splitter 41.5% 28.4%
Semantic Paragraph Splitter (No Header Re-Injection) 63.0% 14.2%
Table-Aware AST Chunker + Breadcrumbs + Numeric Verifier 98.5% 0.0% (Deterministic Block)

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