What is a RAG Pipeline? Stages, Components and Pitfalls

The two halves of a pipeline

It helps to see a RAG pipeline as an offline half and an online half. The offline half prepares your knowledge: it runs when documents are added or changed. The online half answers a question in a second or two.

The offline half: preparing documents

Ingestion: files, web pages, emails and feeds come in from uploads, crawlers or connectors.
Parsing and OCR: PDFs, Office files and scans are turned into text, with tables and headings kept as structure rather than a flat stream.
Cleaning and protection: duplicate content is removed and sensitive values are detected and handled before anything is stored.
Chunking: the text is split into passages along its natural structure.
Embedding: each passage becomes a vector that represents its meaning.
Indexing: vectors and text are stored so they can be searched by meaning and by keyword.

The online half: answering a question

Query understanding: the question may be rewritten or expanded so different wordings still match.
Retrieval: meaning search and keyword search each propose candidate passages, which are merged.
Filtering and permissions: only passages the asker is allowed to see, and that match any filters, remain.
Re-ranking: a more careful model orders the candidates by how well they actually answer the question.
Generation: a language model writes an answer from the top passages, with citations.
Checking: claims are verified against the passages, and unsupported ones are removed or the answer is refused.

Where pipelines break

Because every stage feeds the next, an early mistake is invisible later. A scan that yielded no text produces no passages. A table flattened to numbers produces passages nobody can interpret. A clause split in two produces two passages that each look incomplete. By the time the model answers, the damage is done.

This is why teams that only tune the prompt or swap the model often see no improvement: the problem is upstream.

Build or buy

Each stage can be built from open-source parts, and many teams start that way. The cost shows up in the long tail: odd file formats, scanned pages, tables, permissions, deletion, monitoring and evaluation. A managed pipeline trades that maintenance for a service you call.

Key takeaways

A RAG pipeline has an offline half that prepares documents and an online half that answers questions.
Most quality problems start in reading and splitting documents, not in the model.
Measure each stage, not only the final answer.

How troveGEN helps with building a RAG pipeline

troveGEN runs the whole chain behind an API: ingestion from files, crawls and connectors, parsing and OCR, structured chunking, protection of sensitive data, embedding, indexing into a vector database you choose or that we host, and hybrid search with re-ranking, filters and permissions. You keep control of the last mile, which is what to build on top.

What troveGEN provides

Ingestion from files, websites and connectors such as S3, Azure Blob and Google Drive
Parsing and OCR in the language you set, with tables kept as tables
Structure-aware chunking chosen automatically for each document
Sensitive values protected before anything is embedded
Indexing into the vector database you choose, or ours
Hybrid search, reranking, filters and permissions on every query

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Frequently asked questions

What is the difference between a RAG pipeline and a RAG application?

The pipeline is the infrastructure that prepares knowledge and retrieves from it. The application is what users touch, such as a chat window, a search page or an agent, built on top of that pipeline.

How long does it take to build one?

A working prototype can take days. A production pipeline that handles scans, tables, permissions, deletion and evaluation usually takes months of engineering and ongoing maintenance, which is the main reason teams use a managed one.

Which stage matters most?

For most document collections, reading the documents correctly and splitting them sensibly. If the right passage never reaches the index intact, no later stage can recover it.

How does troveGEN help with building a RAG pipeline?

troveGEN is a managed RAG pipeline. Every stage, from ingestion to ranked, permission-checked results, runs behind one API, with a timeline you can inspect for each document. It provides: Ingestion from files, websites and connectors such as S3, Azure Blob and Google Drive; Parsing and OCR in the language you set, with tables kept as tables; Structure-aware chunking chosen automatically for each document; Sensitive values protected before anything is embedded; Indexing into the vector database you choose, or ours; Hybrid search, reranking, filters and permissions on every query.

What is a RAG pipeline?