Retrieval Augmented Generation (RAG): The technology behind the smartest chatbots
Retrieval Augmented Generation (RAG) is a revolutionary technology that underpins modern AI chatbots and Large Language Models (LLMs). It solves a central problem of pure LLMs: hallucination (the invention of facts) and the limitation to outdated training knowledge. RAG ensures that the AI answers based on current, specific and verifiable knowledge.
How RAG works
A RAG system works in two main phases when a user asks a question:
- Retrieval: The user query is used to search an external knowledge base (e.g. customer documentation, internal manuals) for the most relevant sections of text.
- Augmentation: The retrieved text sections are sent to the LLM together with the original user query. The model then generates an answer based directly on these source texts and quotes them.
At the heart of this retrieval phase is the storage and search of content in the form of vectors.
Why data is stored in vectors
Storing data as vectors (vectorization or embedding) is the key to intelligent search in RAG systems. What is vector embedding?
Vector embedding is a numerical representation (a long list of numbers) of a section of text (word, sentence or paragraph) in a high-dimensional space.
- Meaning instead of keyword: The process uses special neural networks to capture the semantic meaning of a text. This means that vectors that are spatially close to each other represent texts that have a similar meaning, even if they use completely different words.
- Distance = Similarity: The similarity between the vector representation of the user question and the vectors of the documents can be determined simply by calculating the distance (e.g. cosine similarity) in the vector space.
Example: The sentence "I am looking for laptops" will lie in the same region of the vector space as the sentence "Where can I find notebooks", as both deal with the same concept (portable computers).
RAG searchvs. classic web search (e.g. Solr)
Vector search in RAG systems offers a fundamental advantage over classic keyword-based search as used in traditional search engines (e.g. Solr, Elasticsearch).
| Feature | Vector search | Classic search (Solr/Elasticsearch) |
|---|---|---|
| Search basis | Semantic meaning (context and sense) | Keywords (exact text match) |
| Relationship | Finds documents that are conceptually similar | Finds documents that contain the exact word |
| Tolerance | High tolerance for synonyms and different formulations | Low tolerance, often requires complex query tuning |
| Result | Relevant text sections that directly answer the question | Long list of documents containing the keyword |
The decisive difference
When a user asks:
- "How can I service a portable calculator?" Solr would mainly search for documents containing the exact words "portable", "calculator" and "service".
- The vector search finds documents that deal with "laptop care", "notebook maintenance" or "handling portable computers" because the vectors of these concepts are close to each other in the vector space.
This semantic search allows the RAG bot to retrieve highly relevant, contextual content that enables the LLM to generate a precise, fact-based and human-like response. RAG is thus the key to transforming LLMs from general language generators to specialized, reliable knowledge experts.