Prechunking Content Engineering

A Systems-Level Course for LLM Ingestion, Retrieval, and Citation

This is an operator course, not documentation.

The goal is to teach how to design content that:

chunks deterministically
is extractable and reusable by generative systems
vectorizes cleanly
minimizes cross-chunk inference cost
survives retrieval without hallucination
is safe to cite

How to Use This Course

This course is designed for flexible learning:

You can read sequentially (Module 1 → 9) or jump to specific topics
Each module is atomic and self-contained—no prerequisites required
This is not a checklist course. Mastery comes from understanding constraints, not finishing pages
Modules reference each other, but you can read them in any order

Think of this as a knowledge system, not a linear curriculum. Return to modules as needed. Deep understanding matters more than completion.

Course Mental Model

LLMs ingest the web like a distributed data lake:

Pages = raw files
DOM = semi-structured records
Chunks = rows
Embeddings = indexes
Retrieval = approximate joins
Context window = memory budget
Citation = confidence threshold crossing

Prechunking is schema design for untrusted data sources. Chunking is the method. Extractability is the outcome.

Course Modules

This course is broken into modules, each with a single learning objective. Each module is its own page for optimal LLM ingestion and retrieval.

1

How LLMs Actually Chunk Content

Why "sections" are not real chunks. Token-based chunking, DOM boundary heuristics, sentence vs semantic chunking.

Key Truth: LLMs do not respect paragraphs, headings, or visual sections. They chunk based on token limits, punctuation density, semantic similarity, and transformer attention patterns.

Practical Rule: If a fact requires previous text, it is already broken.

2

Chunk Atomicity and Inference Cost

Why multi-fact chunks fail retrieval. Each chunk is a row. Each row should answer one question.

Key Truth: If an LLM must resolve pronouns, infer scope, merge facts, or assume context, it increases CPU cost, hallucination risk, and citation avoidance.

Practical Rule: One chunk = one assertion = one retrieval target.

3

Vectorization and Semantic Collisions

Why vague chunks lose embedding battles. Embeddings collapse meaning into dense vectors.

Key Truth: If a chunk contains multiple ideas, mixed intent, or generic language, its vector becomes non-dominant. Your content exists but never wins nearest-neighbor retrieval.

Practical Rule: Each chunk must be semantically narrow, lexically explicit, and intent-pure.

4

Data Structuring Beyond Pages

Prechunking is not just page layout. Structured layers: JSON-LD, lists, tables, definitions, repeated factual patterns.

Key Truth: Structured data reduces inference depth, ambiguity, and retrieval risk. LLMs trust structured repetition more than prose.

Practical Rule: Important facts must exist in multiple structural forms.

5

Cross-Page Consistency as Signal Amplification

Why single-page optimization fails. LLMs evaluate cross-source agreement, consistency across contexts, and repeated factual phrasing.

Key Truth: LLMs evaluate cross-source agreement, consistency across contexts, and repeated factual phrasing. This is not duplication. This is data reinforcement.

Practical Rule: Facts must repeat across pages, across sections, across formats. But never change meaning.

6

Prompt Reverse-Engineering (Safely)

How to infer questions without prompt injection. You are not manipulating prompts. You are modeling question distributions.

Key Truth: You are not manipulating prompts. You are modeling question distributions: primary user questions, follow-up questions, trust questions, safety constraints.

Practical Rule: If a question can be asked, its answer must already exist as a chunk.

7

Citation Eligibility Engineering

Why AI avoids citing most content. LLMs avoid citing content that sounds promotional, makes guarantees, lacks scope, or mixes opinion and fact.

Key Truth: LLMs avoid citing content that sounds promotional, makes guarantees, lacks scope, or mixes opinion and fact.

Practical Rule: Write chunks that are factual, scoped, boring, and safe. Boring content gets cited.

8

Measuring Prechunking Success

What to measure instead of rankings. Real metrics: retrieval appearance, answer reuse, citation frequency, near-verbatim reuse, cross-engine consistency.

Key Truth: Real metrics are retrieval appearance, answer reuse, citation frequency, near-verbatim reuse, and cross-engine consistency. Traffic, impressions, and CTR are downstream effects, not controls.

Practical Rule: Measure retrieval and citation, not traffic and impressions.

9

Failure Modes (Why Chunks Die)

Why content disappears from AI answers. Common failures: pronouns, implied context, mixed services, marketing adjectives, narrative transitions.

Key Truth: Common failures include pronouns, implied context, mixed services, marketing adjectives, and narrative transitions.

Practical Rule: If a chunk cannot stand alone, delete it.