> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentset.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Why RAG-as-a-Service

> Prototypes take a week. Production takes months. RAG-as-a-service closes the gap.

## The 80% problem

Frameworks like LangChain and LlamaIndex are great for prototypes. You can follow a tutorial, connect your documents, and have a working demo in a few days. Run it on a few documents and the results look promising.

Then you deploy to production. Results appear to be subpar, users quickly notice.

Getting from 80% to 95% takes months of work:

* Building and testing document parsing pipelines
* Experimenting with chunking strategies and chunk sizes
* Tuning hybrid search (semantic + keyword)
* Configuring and testing rerankers

Each component affects accuracy, and getting the compounding benefit of optimizing every step is a full-time job.

## What RAG-as-a-service provides

Instead of building and maintaining this infrastructure yourself, RAG-as-a-service gives you:

* **Parsing and chunking** — Optimized pipelines that produce clean, logical chunks across 22+ file formats without custom code per format.
* **Query generation** — Automatic multi-query expansion from conversation context, covering more ground than a single hybrid search.
* **Reranking** — Built-in reranking that significantly improves chunk relevance, often compensating for suboptimal upstream choices.
* **Scale** — Ingest and search millions of documents without managing vector storage, object storage, indexing, or compute.
* **Continuous improvement** — Automatic access to new retrieval methods as they emerge, without changing your code.

Ready to give it a try? The [quickstart](/get-started/quickstart) walks you through your first search in under 5 minutes.