What is Retrieval-Augmented Generation (RAG)
What if we took the time to understand the concepts and benefits of RAG?
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a hybrid approach that combines two technologies:
- Information retrieval in a specific database,
- Text generation by a language model (LLM).
Rather than relying only on its internal knowledge (which is fixed at training time), a RAG model first retrieves relevant passages from a data source ( internal documents, PDFs, FAQs, etc.), then uses those passages to generate a contextualized, fluent, and sourced response. This method yields answers that are grounded in available real information rather than simple model extrapolations.
How does it work? (general process)
A RAG system works in several main steps:
- Data indexing: Documents or content are transformed into vectors (mathematical representations) and stored in a specialized database (such as a vector database).
- Retrieval: When a query is submitted, it is also converted into a vector and compared to the stored vectors to find the most relevant passages.
- Prompt augmentation: The selected excerpts are combined with the user’s question to form an " extended prompt" that is fed to the generation model.
- Final generation: The language model generates a coherent response based on the selected excerpts, which reduces the risk of producing incorrect or invented information.
RAG vs other AI approaches
| Approach | Access to up-to-date data | Source citation | Complexity |
|---|---|---|---|
| Simple prompt | ❌ | ❌ | Low |
| Fine-tuning | ❌ | ❌ | High |
| RAG (with retrieval) | ✅ | ✅ | Medium to high |
➡️ RAG is especially well suited when accuracy, data freshness, and source traceability matter.
Where is RAG used?
This approach is used in many contexts:
- Customer support and dynamic FAQs: responses are based on internal documents or product guides.
- Internal assistance: to answer employee questions about policies, procedures, or company-specific knowledge.
- Business applications: generating contextualized documents such as legal summaries, technical analyses, or personalized responses.
Why it is useful
- Reduced errors: by grounding responses in verified information, the model reduces “hallucinations” (fabricated facts).
- Access to up-to-date information: data can be updated without retraining the model.
- Better personalization: responses account for business or organizational context.
Limits and possible evolutions
Even though RAG improves response reliability, it still depends on the quality of its database: if documents are incomplete or poorly structured, generated answers can be less accurate. In addition, access management and data security remain important challenges, as does the future integration of multimodal sources (text, image, audio).
Learn more? Feel free to check our page focused on RAG in our Technology part.