3.2.1. Chunking Strategies for Optimal Retrieval

💡 First Principle: Chunking is a precision/recall trade-off — small chunks maximize retrieval precision (each chunk is tightly focused) but lose surrounding context; large chunks preserve context but decrease precision (more irrelevant content per chunk). The right strategy depends on your document structure and query pattern.

Chunking strategies compared:

Bedrock Knowledge Bases chunking configuration:

chunking_config = {
    "chunkingStrategy": "HIERARCHICAL",  # Or FIXED_SIZE, SEMANTIC, NONE
    "hierarchicalChunkingConfiguration": {
        "levelConfigurations": [
            {"maxTokens": 1500},  # Parent chunk
            {"maxTokens": 300}    # Child chunk
        ],
        "overlapTokens": 60
    }
}

Custom chunking in Bedrock Knowledge Bases: choose the no chunking strategy and attach a custom transformation Lambda function (customTransformationConfiguration, step POST_CHUNKING) that writes your own chunks. For long PDFs, that Lambda can use Amazon Textract Layout analysis, which labels blocks such as LAYOUT_SECTION_HEADER, LAYOUT_TEXT, LAYOUT_HEADER and LAYOUT_FOOTER. That lets it cut one chunk per section and drop repeated page headers and footers, which splitting by token count or topic would carry into the chunks.

Chunk overlap — why it matters: Without overlap, a concept split across two consecutive chunks may never be fully retrieved. A user query about "the handoff between authentication and authorization" fails if the sentence describing that handoff is split between chunk 47 and chunk 48 with no overlap.

⚠️ Exam Trap: Semantic chunking in Bedrock Knowledge Bases uses an FM to detect topic shifts — it costs tokens (FM invocation per document during ingestion) and takes longer than fixed-size chunking. Exam scenarios that require "fastest ingestion with acceptable quality" should use fixed-size chunking, not semantic.

Reflection Question: You're building a RAG system over a legal code document where each section (numbered 1.1, 1.2, etc.) is independently testable. Users query about specific sections. What chunking strategy would produce optimal precision, and how would you configure the chunk boundaries?