Choose Your Level
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Design a Chunking Strategy
Decide how to split an employee handbook for a vector database.
Prompt for Metadata Extraction
Extract tags from documents to improve RAG search filtering.
Generate Synthetic Q&A Pairs
Improve RAG retrieval by embedding hypothetical questions instead of raw text.
Retrieval-Augmented Generation (RAG) Document Preprocessing
RAG combines LLMs with external knowledge bases. Data engineers and AI developers demonstrate competence by chunking raw documents (Fixed-Size vs Semantic Chunking), configuring overlap parameters (10-20%), tagging metadata, and cleaning text for vector embedding.
1. RAG Document Chunking & Cleaning Specification
Specification defining optimal chunk sizes (e.g. 512 tokens), overlap ratios, header preservation rules, and noise removal.
2. Chunk Metadata Taxonomy & Tagging Architecture
Metadata schema tagging document source, author, creation date, section title, and access permissions per chunk.
3. RAG Data Ingestion Test Log
Audit log verifying document parsing accuracy, chunk boundary integrity, and embedding vector readiness.
Frequently Asked Questions (RAG (Retrieval-Augmented Generation) Prep)
What is the difference between Fixed-Size and Semantic Chunking in RAG?
Fixed-size chunking splits text by strict token/character counts (e.g. 500 tokens). Semantic chunking splits text at natural semantic boundaries (paragraphs, headings, or topic shifts) to preserve context integrity.
Why is chunk overlap important in RAG preprocessing?
Chunk overlap (typically 10-20%) ensures key sentences spanning chunk boundaries are not split in half, preserving complete contextual meaning for vector retrieval.
Why should metadata be attached to document chunks?
Metadata enables hybrid search filtering (e.g. filtering retrieval to docs created in 2026 or tagged under specific departments) before vector similarity ranking.
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