Token efficiencySEO landing

Context optimization for AI retrieval

Context optimization is the discipline of removing layout noise before content ever reaches an LLM. The goal is not aggressive compression. The goal is a cleaner ratio of signal to tokens.

Primary use

Reduce noisy HTML and preserve semantic structure so every prompt and chunk carries more useful signal per token.

Recommended flow

Fetch, clean, measure tokens, then hand consistent Markdown to agents or retrieval systems.

Next step

Use the Playground to compare raw HTML against optimized output before integrating the API.

Why optimization matters before retrieval

Embedding noisy boilerplate creates weak vectors, bloated chunks and unstable retrieval. Cleaning content first improves both semantic recall and inference cost.

AIngestor pushes normalization ahead of chunking so downstream systems see a consistent document shape.

What to optimize

Remove repeated navigation, cookie overlays, newsletter walls and unrelated footer matter. Keep the pieces that carry meaning for question answering or tool use.

Section titles that anchor meaning.
Ordered and unordered lists that express procedures or requirements.
Tables that compare capabilities, limits or versions.

Internal links

Related technical paths

Open Playground

Cost control

Reduce HTML token usage before RAG or agents

Shrink token spend by converting bloated HTML into compact Markdown before chunking, prompting or embedding.

Preparation layer

LLM-ready content from messy HTML

Prepare documents for models with clean headings, preserved code and lower-noise context windows.

Chunk quality

Semantic chunking starts with clean structure

Chunk by meaning instead of arbitrary token windows by preserving headings, lists and tables before segmentation.