RAG Data Sources: Types, How to Build Them, and How to Keep Them Clean

TL;DR

• RAG inserts a retrieval step between the user query and the model, so answers draw on fetched documents rather than training data alone.
• Data sources determine output quality. Structured, semi-structured, and unstructured content all work, from internal wikis to live web pages.
• External web data fills the gap internal documents can't cover, and collecting it at scale needs scraping infrastructure and rotating IPs.
• Most RAG failures trace back to the corpus: stale indexes, sloppy chunking, and unvetted sources produce bad answers no model can fix.

What is retrieval-augmented generation (RAG)?

Retrieval-augmented generation is a pattern that combines information retrieval with text generation.

When a user asks a question, the system first searches an external collection of documents for relevant information, then passes that information to a large language model (LLM) alongside the original question. 

In other words, the model uses the retrieved information alongside its existing knowledge to produce an answer.

"External data" here means anything stored outside the model itself. This could be a product manual, a support ticket archive, a scraped competitor pricing page, or even a regulatory filing. It’s different from parametric knowledge, which is the information stored in the model’s weights during training.

RAG exists because pre-trained models have three structural gaps:

• They're frozen at a training cutoff and can't know what happened after it
• They produce hallucinations, stating false information with the same confidence as true information
• They don’t have access to private or company-specific information

The original 2020 paper that introduced the term presented RAG as a way to combine the knowledge stored in a model’s parameters with an external, updatable source of information. 

Its authors argued that this could improve factual generation, make knowledge easier to update, and provide clearer provenance for model outputs. 

Why use RAG: the limits it addresses

RAG can help address several of the limitations that come with using a language model on its own:

• Factual accuracy and grounding. When a model bases its answer on retrieved passages, you can trace the information back to the original source. The same goes for reviewing citations. This can reduce hallucination rates and, more importantly, makes fabrication easier to detect when they occur.
• Up-to-date information. We live in a fast-moving world, where a model trained last year may not be able to provide accurate information about what is happening today. RAG helps by connecting the model to updated external sources, making up for the limits of its training cutoff.
• Relevance and response quality. A general-purpose model gives general-purpose answers. But when you feed it your internal data or your domain corpus, it produces answers no off-the-shelf model can match. 
• Cost efficiency. Retraining or fine-tuning a model can require significant time, data and computing power. When your information changes, updating a retrieval index through RAG is often faster and cheaper than retraining the model.

How RAG works: architecture and the core stages

A working RAG system can include several steps, but the process usually follows four core stages:

Stage 1: Ingestion and indexing

Before the system can retrieve anything, you need to prepare the information it will search.

The first step is collecting documents from preferred sources such as webpages, product manuals, or internal databases, then feeding them into the system’s knowledge base. 

The system, in turn, splits those documents into smaller sections called chunks. Each chunk is converted into numerical representations called embeddings and stored in a searchable index, often inside a vector database. This index is updated whenever you add new documents or the original information changes.

Retrieval generally comes in two forms:

• Dense embeddings place similar ideas close together, which helps the system recognize that phrases such as "cheap flights" and "affordable airfare" mean roughly the same thing. 
• Sparse embeddings work more like traditional keyword search and are better at matching exact terms, product codes and uncommon names.

Stage 2: Retrieval

When a user asks a question, the system searches the index for chunks that are likely to contain relevant information.

You can think of this like using a library catalogue. Instead of reading every book, you search the catalogue and retrieve the pages or sections most closely related to your question.

For dense retrieval, the system converts the query into an embedding and compares it with the stored embeddings. Many production systems also use hybrid search, which combines semantic matching with keyword search. Dense retrieval can recognize similar meanings, while sparse retrieval is better at finding exact words and phrases.

Some systems also add a reranker at this stage. The first search may return dozens of possible chunks, so the reranker examines that shortlist more closely and moves the most relevant results to the top.

Stage 3: Augmentation

Once the system has selected the most useful chunks, it adds them to the prompt alongside the user’s original question and any system instructions.

This is the augmentation part of retrieval-augmented generation. The model is no longer answering from the question alone. It now has additional context that it can use to form its response.

How the system assembles the prompt also matters. The number, order, and relevance of the retrieved chunks can affect which information the model notices and uses.

Stage 4: Generation

The language model reads the augmented prompt and generates an answer using the retrieved information as context.

Depending on how the system is built, it may also be instructed to cite its sources, admit when the retrieved information is insufficient, or limit its answer to claims supported by the supplied documents.

Workflow frameworks such as LangChain can help connect these stages so you don't have to build every part of the pipeline from scratch. 

RAG data sources: what to use and how to prepare them

A RAG system is only as useful as the information it can retrieve. Before building one, you need to decide what data it should use, where that data will come from, and how you will keep it accurate.

Types of RAG data sources

RAG systems can draw from several forms of data:

• Structured data includes organised information from databases, tables and knowledge graphs.
• Semi-structured data, such as JSON, CSV, XML and HTML, follows a recognisable format but doesn't always fit neatly into rows and columns.
• Unstructured data includes PDFs, documents, emails, transcripts and webpages.

The right source depends on what you are building. A customer-support assistant may rely mostly on internal product documentation and support tickets. A market-intelligence system may need external sources such as news sites, competitor pages, pricing data and public datasets.

In many cases, you will need both. Internal sources give the model knowledge about your organization, while external sources help it answer questions about the wider market.

Collecting external and current data

Internal documents can't tell you when a competitor changes its prices or when new information appears online. For use cases like these, you need a way to extract up-to-date web data and add it to your RAG knowledge base.

You can collect this information through web scraping. Decodo’s Web Scraping API can retrieve content from webpages at scale, while residential proxies can help you access location-specific results and reduce problems caused by blocks. For more difficult websites, Site Unblocker can handle some of the access challenges involved.

The collected data also needs to be refreshed. Scheduled scraping and indexing jobs allow you to revisit sources regularly so your RAG system doesn't continue retrieving outdated information.

Preparing data for retrieval

Raw data is rarely ready to place directly into an index. You first need to parse it, remove unnecessary content, and follow a consistent data-cleaning process.

Once the content is clean, you divide it into smaller chunks. Fixed-size chunking creates sections of a similar length, often with some overlap between them. Sentence-based chunking keeps related sentences together, while format-aware chunking follows the natural structure of a document, such as headings, sections, or table rows.

There is no single chunk size that works for every system. Larger chunks provide more context but may contain irrelevant details, while smaller chunks are more precise but can separate information that belongs together.

Finally, add metadata such as the source, publication date, location, and document type. This helps the system filter results and track where information came from. When a source changes, update its version and re-embed the affected chunks so the index reflects the latest content.

Together, these steps turn raw internal and external data into a source your RAG system can search and use reliably.

RAG use cases and real-world applications

Here are some of the most common RAG use cases and how organizations apply them in practice:

• Chatbots and Q&A assistants. Customer support bots can retrieve answers from product documentation, help centers and support tickets, while internal assistants can search company policies, onboarding guides and process documents. 
• AI agents. RAG can act as the retrieval layer that gives an agent access to fresh web data, internal records, or specialist sources before it takes an action. Decodo’s AI agent enablement tools and MCP server can help connect agents to live web information.
• Enterprise knowledge engines. Organizations use RAG to search across HR documents, compliance records, contracts and internal repositories. Instead of checking several systems manually, employees can ask a question and receive an answer based on the relevant internal material.
• Industry-specific systems. eCommerce teams can monitor prices, products and competitor pages, while financial analysts can retrieve filings, news and market research. Legal teams can search legislation, case law and contracts, and healthcare systems can ground responses in approved medical sources.

In high-stakes fields such as healthcare, finance and law, retrieval should support human judgement rather than replace it. The quality of the answer still depends on the accuracy of the sources and whether the system retrieved the right information.

RAG vs. semantic search, prompt engineering and fine-tuning

RAG overlaps with several other AI techniques, but each one solves a different problem:

Here’s a simple way to look at it:

• Use RAG when the knowledge changes.
• Use fine-tuning when the model’s behavior needs to change.
• Use prompt engineering when clearer instructions may be enough.
• Use semantic search when retrieval is the final product.

Pretraining or building an LLM model from scratch is a much larger undertaking and is rarely necessary for this type of problem. 

Common challenges and limitations of RAG

RAG can improve the quality of an AI system, but its performance still depends on the data and pipeline behind it.

• Data quality and freshness. RAG follows the same "garbage in, garbage out" rule as any other data system. Stale indexes, poor chunking, and unreliable sources can all lead to weak answers, while even a factually correct passage may be misleading when taken out of context. Regular data cleaning and source updates are therefore essential.
• Data-source security. External sources can be manipulated or contain hidden instructions designed to influence the model. This is often called RAG poisoning or indirect prompt injection. You can reduce the risk by using trusted sources, allowlisting approved domains, removing suspicious content, and monitoring what enters the index. 
• Integration and maintenance. Connecting databases, websites, PDFs, and internal tools can become complicated because each source uses a different format. You also need to choose embedding models, maintain connectors, and re-embed content when it changes.
• Retrieval and generation limits. The system may retrieve the wrong passage, miss the right one, or include more content than the model can handle. Retrieval also adds latency and makes evaluation harder because you need to test both the search results and the final answer. More importantly, RAG can reduce LLM hallucinations, but it can't remove them completely or replace fine-tuning and retraining in every situation.

The current and future landscape of RAG

RAG has moved beyond a temporary fix for outdated model knowledge and become a common part of enterprise AI systems.

One major development is agentic RAG. Instead of retrieving information once, an AI agent can decide when to search, break a complex request into smaller questions, and use several tools or databases before producing an answer. 

RAG is also expanding beyond text. Multimodal systems can retrieve information from images, audio, and video, while real-time pipelines can draw from live data feeds instead of relying only on stored documents. Persistent memory can also help applications retain useful information across longer interactions.

At the same time, better evaluation tools are making it easier to measure retrieval quality, factual accuracy and whether an answer is supported by its sources. Research into training retrievers and generators together may also reduce some of the manual work involved in choosing prompts, models and retrieval settings.

As models continue to improve, the main difference between RAG systems will increasingly come from the information behind them: how current it is, how reliably it is collected and how accurately the system retrieves it. 

Final thoughts

RAG can only be as good as the data behind it. The type of information you use, how current it is, how well it is cleaned, and whether you can trust the source all shape the quality of the final answer.

Much of the real work therefore happens before the model generates anything. You need to collect the right data, prepare it for retrieval, and keep it updated as the underlying information changes.