When an AI ingests your content, it often breaks it down into “chunks” before embedding them into vector space. If your chunks are too large, context is lost. If they are too small, meaning is fragmented. So, what is the optimal length?

The 512-Token Rule

Many popular embedding models (like OpenAI’s older text-embedding-ada-002) had specific optimizations around 512 or ~1000 tokens. While newer models like gpt-4o support 128k+ context, retrieval systems (RAG) often still use smaller chunks (256-512 tokens) for efficiency and precision.

Why? Because fetching a 10,000-word document just to answer “What is the return policy?” is computationally wasteful and distracts the model with irrelevant noise.

The Goldilocks Zone

  • Too Short (< 50 words): The vector is noisy. “It is fast” matches everything from a car to a CPU to a cheetah.
  • Too Long (> 1000 words): The vector is diluted. A document about “Climate Change, Stock Markets, and Baseball” averages out to a meaningless vector in the middle of nowhere.
  • Just Right (200-400 words): A coherent paragraph or section with a single topic.

Semantic Slicing for E-Commerce Catalogs

In E-Commerce, you often deal with thousands of SKUs. The mistake everyone makes is feeding the entire product page as one chunk.

The “Blob” Problem (Bad)

  • Chunk: [Product Title + Description + Specs + Reviews + Shipping Info] (1500 tokens).
  • Result: The embedding vector is a muddy average of “shipping,” “specs,” and “marketing.”

The “Sliced” Strategy (Good)

  • Chunk A (Description): [Product Title + Detailed Description + Value Prop]. Target: Users asking “What is X?” ~300 tokens.
  • Chunk B (Specs): [Product Title + Technical Specifications Table]. Target: Users comparison shopping. ~200 tokens.
  • Chunk C (Policy): [Product Title + Return Policy]. Target: Users asking “Can I return X?” ~100 tokens.

By slicing your catalog, you create sharp, distinct vectors for each verifiable attribute of the product.

The Strategy: “Micro-Articles”

Rewrite your long-form content as a series of connected “micro-articles.”

  1. Defining Variables: Each section defines its terms.
  2. Explicit Context: Repeat the main subject in each section. Don’t say “It works well.” Say “The Application works well.”
  3. Summary Headers: Use descriptive headers that summarize the chunk.

By optimizing for the chunk, you optimize for the retrieval.

The “Needle in a Haystack” Problem

Researchers have identified the “Lost in the Middle” phenomenon with LLMs. If the relevant answer is buried in the middle of a long context window, the model’s accuracy drops significantly. This reinforces the need for shorter, punchier documents.

The Ideal Vector Shape: Aim for documents that map to a “tight sphere” in vector space. If you visualize your document’s sentences as points in 3D space, they should ideally form a dense cluster. High-ranking content for RAG systems is invariably “dense” and “topically unitary.”

Glossary of Terms

  • Agentic Web: The specialized layer of the internet optimized for autonomous agents rather than human browsers.
  • RAG (Retrieval-Augmented Generation): The process where an LLM retrieves external data to ground its response.
  • Vector Database: A database that stores data as high-dimensional vectors, enabling semantic search.
  • Grounding: The act of connecting an AI’s generation to a verifiable source of truth to prevent hallucination.
  • Zero-Shot: The ability of a model to perform a task without seeing any examples.
  • Token: The basic unit of text for an LLM (roughly 0.75 words).
  • Inference Cost: The computational expense required to generate a response.