How to Optimize RAG Knowledge Base Construction: Parsing, Chunking, and Retrieval

Why Knowledge‑Base Construction Matters

RAG (Retrieval‑Augmented Generation) works only if the knowledge base can provide complete, correct information in a form the model can understand and generate from. Poor parsing or chunking leads to low recall, hallucinations, and a bad user experience.

Offline Parsing (Parsing)

Parsing transforms raw documents into structured knowledge. Enterprise documents are often non‑textual (scanned PDFs, double‑column layouts, watermarks, PPTs, Excel sheets, videos with transcripts). Directly calling read_text() on such files yields unusable output.

OCR & layout issues cause fragmented tables, merged columns, lost headings, and missing image captions.

These errors degrade retrieval (cannot find the answer) and generation (model produces unfaithful responses).

Optimization strategies include:

Image enhancement + fine‑tuned OCR models for scanned PDFs.

Table detection models that convert tables to JSON/HTML to retain structure.

Layout‑parser models to restore reading order in double‑column documents.

Preserving hierarchical metadata (section_id, heading tags) during parsing.

Chunking – Turning Information into Usable Knowledge

Common mistakes: fixed‑length splits, naive sentence splits, ignoring tables/images, which fragment semantics and hurt retrieval.

Better approach (semantic chunking):

Use natural paragraphs as the primary unit.

Apply flexible token‑based splitting within paragraphs.

Ensure each chunk conveys a single coherent idea.

Treat tables and images as indivisible units because they contain dense information. Keep them as whole chunks.

Preserve heading hierarchy as metadata so each chunk knows its position in the document.

chunk_text: "重大疾病保险理赔需提供住院病历..."
metadata: { section: "2.1 特殊情况处理", page: 3 }

This enables retrieval not only of content but also of its surrounding context.

Putting Parsing and Chunking Together

A strong interview answer should describe the two stages:

We split knowledge‑base construction into an offline parsing stage (OCR, layout analysis, table extraction, hierarchy preservation) and a chunking stage (semantic chunking, keeping tables/images intact, recording source metadata).

Such a pipeline ensures high recall, faithful generation, and a smooth user experience.

Conclusion

The ceiling of a RAG system is determined by the quality of its knowledge base. Clean parsing, sensible chunking, and clear structure lead to reliable retrieval, accurate generation, and better interview performance.