5.3.2 Chunk Size and Overlap¶
Why This Sub-Part Matters¶
Chunk Size and Overlap is the working skill inside RAG Ingestion that helps you build the stage artifact, An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.
Study Pages¶
| Page | Purpose |
|---|---|
| Deep Dive | Full explanation, mechanisms, examples, and failure modes. |
| Examples and Practice | Worked exercises, project drills, and self-check prompts. |
Core Ideas¶
- Define Chunk Size and Overlap in plain language before naming tools or frameworks.
- Connect it to the stage artifact: An evaluated RAG or AI workflow application with documents, prompts, tests, logs, latency, cost, and failure analysis.
- Measure it with: parsed documents, rejected content, chunks, metadata, and freshness
- Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
- Keep the first implementation small enough to inspect by hand before scaling it.
How to Study It¶
- Read this overview and write the concept in your own words.
- Read the deep dive and identify the input, transformation, output, and failure mode.
- Complete the examples and practice page.
- Add one measurement using: Track parsed documents, rejected content, chunks, metadata, and freshness.
Completion Standard¶
- I can explain Chunk Size and Overlap without naming a tool first.
- I can connect it to the stage artifact.
- I can show a small artifact, measurement, or test.
- I know how it fails and what I would inspect first.
Return to 5.3 RAG Ingestion.