What is RAGO?
Retrieval-augmented generation optimization prepares content for RAG pipelines, which power AI search engines, chatbots and workplace assistants. These systems split documents into chunks, index them, and retrieve only the best-matching chunks to feed the model.
Because the model sees chunks rather than whole pages, each section has to make sense on its own and use the words and meaning that match how people ask.
Why RAGO matters
If your answer is split across chunks, buried in a long section or depends on earlier context, the retriever may never surface it. RAGO makes your content retrievable chunk by chunk.
How RAGO works
- 01
Chunk
Pages are split by headings or length, often a few hundred words per chunk.
- 02
Index
Chunks are converted into embeddings for semantic search and indexed for keyword search.
- 03
Retrieve
For each query, the system pulls the best-matching chunks using semantic and keyword search.
- 04
Generate
The model answers from those chunks and may cite their source.
RAGO best practices
- Write sections that stand alone, and repeat the subject's name.
- Keep sections to roughly 100–300 words under a descriptive heading.
- Use the words people use in their questions, not only internal jargon.
- Put the key answer at the start of the section.
- Don't split one answer across several sections.
Common RAGO mistakes
- Sections that start with “This” or “It”, referring back to the previous section.
- Very long sections that dilute relevance.
- Headings that don't match the content beneath them.
How to measure RAGO
Metrics to track
- Where the correct chunk ranks for each test query
- Share of chunks that don't stand on their own
- Answer accuracy in RAG-based assistants
What it improves
- Chunk-level relevance
- Heading-to-content alignment
- Semantic similarity to queries
- Chunk self-containment
RAGO vs. related optimizations
These overlap with RAGO. Work on one usually helps the others.
RAGO FAQ
What is a chunk?+
A piece of a document, often one section or a few hundred words, that a RAG system indexes and retrieves on its own.
What is hybrid search?+
Retrieval that combines keyword matching (such as BM25) with semantic search using embeddings. Many RAG systems use both, so content needs both the right words and clear meaning.
How big should sections be for RAG?+
There's no universal chunk size, but sections of roughly 100–300 words with a clear heading work well across most pipelines.
Does RAGO matter for my own chatbot?+
Yes. The same principles help internal and customer-support assistants built on your documentation retrieve the right answer.