How to Use RAG in Journalism? 

In the previous blog post, we looked in detail at how a RAG system works step by step. In this post, we want to shift the perspective: how can RAG actually be used in the context of journalism, and especially in science & data journalism? We will look at specific use cases, discuss the trade-offs between commercial and self-hosted systems, and outline the key steps you need to think about when building your own RAG pipeline. This also sets the stage for the next part of our series, where we will walk through the code of our own RAG implementation. 

Use Cases for RAG in Journalism

Because RAG is based on a self-defined collection of documents, it opens up a wide range of possibilities for different newsrooms: 

• Archive-Based QA Systems: Probably the most common use case. Journalists or readers ask questions, and the system generates answers based on the outlet’s own archive material. Major publishers such as Süddeutsche Zeitung, Financial Times, or The Washington Post have already experimented with this type of tool. 

• Semantic Search in Complex Documents: RAG can make it easier to navigate through material that is hard to read or very technical, such as long legal documents, regulatory texts, scientific papers, or government reports. Instead of scanning hundreds of pages, you can ask targeted questions and retrieve exactly the passages you need. 

• Document Summarization: RAG can generate summaries of long or technical documents. A key advantage here is the transparency. By checking which chunks were retrieved, you can directly verify which passages the summary is based on, something traditional LLM-based summarization tools often lack. 

• Fact-Checking and Verification: RAG can help verify whether claims made about specific documents are actually supported by its content. A good example are the so-called RKI files that circulated in Germany. With a RAG pipeline, it becomes much easier to check if statements attributed to those files really appear in them, or if they were misinterpreted or simply invented. 

These examples illustrate that RAG can support journalists in various aspects of their daily work — from research to verification to making archives more accessible. 

Commercial solutions

If you’re thinking, “but aren’t there already providers offering exactly this?”, you are right. One of the most well-known examples is NotebookLM by Google. OpenAI, Anthropic, and several other providers also offer RAG-like functionality. 

Without any question, those commercial tools have clear advantages. They are easy to use, with no technical setup required. They can be accessed immediately, and they are reliable at small scale. Moreover, they allow journalists with little or no coding experience to get immediate value. 

But these benefits come with trade-offs: 

• Cost Considerations: Many providers charge subscription fees or usage-based costs (per query, per thousand tokens, per document processed). While NotebookLM is currently free, it has limits on file size and the number of documents you can upload per notebook. 

• Data Privacy: Using external services means uploading your data to third-party servers, often outside your jurisdiction. Sensitive internal documents or embargoed material may not be safe in such an environment. 

• Vendor Dependence: Once you rely on an external provider, you are tied to their availability, pricing changes, and strategic decisions. If a service shuts down or changes its terms, your entire workflow could be disrupted. 

• Limited Transparency: With commercial tools, it is difficult (sometimes impossible) to systematically test and evaluate performance. You may get a “vibe” for whether the answers are good or not, but you lack transparency into how the system works and rigorous ways to measure quality, which makes it hard to improve performance. 

These limitations already hint at why building your own system can be attractive. 

Why build your own RAG system? 

By implementing your own pipeline, you gain three key advantages: 

• Complete Control: You keep full control over your data. You decide what stays internal and what can be shared externally. 

• Modularity: Every component of the pipeline (retriever, embedding model, vector store, LLM) can be swapped, upgraded, or adapted to your specific needs. 

• Measurability: Most importantly, you can systematically evaluate performance. By defining metrics, you can monitor how well your system works, identify weaknesses, and iteratively improve it. 

This is exactly why we at the Science Media Center decided to build our own system rather than rely entirely on external providers. 

Steps for Implementing Your Own System

When setting up a RAG pipeline, we found it useful to structure the process into the following steps: 

1. Define the Use Case: Before writing any code, clarify the purpose of the tool. What exactly should it do, and who will use it for what? A QA bot for readers has different requirements than an internal fact-checking tool for journalists. 

2. Index the Knowledge Base: Based on the use case, curate the collection of documents your system should rely on. This might be internal reports, scientific publications, policy documents, or archived articles. 

3. Build a Test Set: To evaluate performance, you need a benchmark. This can be a set of artificial question-answer pairs that reflect your use case and knowledge base. 

4. Define Metrics: Decide what counts as a good answer for your use case and what does not. Do you care most about factual accuracy, completeness, or readability? These criteria can be defined and then turned into measurable metrics. 

5. Start with a Naïve Pipeline: Build a simple RAG setup consisting only of the three core elements: retrieve, augment, generate. This becomes your baseline. 

6. Extend to an Advanced Pipeline: Once the basics work is done, you can add features. These features can include, reranking, better chunking strategies, or specialized embedding models. The goal is to outperform your baseline by improving components. 

7. Test, Compare, Iterate: Run your artificial test set through different system versions, calculate your metrics, and compare results. This approach allows you to see not only how well your pipeline performs overall, but also whether each new feature actually improves performance or not. 

Summary

RAG offers many opportunities for journalists: from archive-based QA systems to fact-checking to semantic search in technical material. While commercial solutions are a quick way to get started, they come with trade-offs around privacy, control, and transparency.  

Building your own pipeline requires more effort but gives you modularity, measurability, and autonomy. 

In the next post, we will look into the technical implementation. We will define our use case, build the vector store, create artificial evaluation data, and set up metrics for our experiments.