<\/span><\/h2>\n\n\n\nThe first step consists of analyzing the user’s question and searching the available resources:
– PDF documents,
– manuals and regulations,
– SQL databases,
– company wikis,
– code repositories,
– articles and training materials.<\/p>\n\n\n\n
RAG does not work like a traditional keyword search engine. It uses semantic search<\/strong>, which analyzes the meaning and context of the question. Thanks to embeddings and vector databases (FAISS, Qdrant, Pinecone, Weaviate) it can find even those passages that do not literally contain the words used, but semantically match the intent of the question.<\/p>\n\n\n\nThe effect? The search is far more precise and takes into account the industry context, the company’s internal vocabulary and the complex relationships between pieces of information.<\/p>\n\n\n\n
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<\/span>2. Augmented – enriching the query with context<\/span><\/h2>\n\n\n\nAfter the relevant passages have been found, the system does not return them to the user in raw form<\/strong>. Instead it attaches them as context to the query sent to the model. The LLM “reads” this data first, so the generated answer is:<\/p>\n\n\n\n\n- current<\/strong>, because it is based on fresh documents,<\/li>\n\n\n\n
- accurate<\/strong>, because it comes from the organization’s own sources,<\/li>\n\n\n\n
- safe<\/strong>, because it limits the risk of hallucinations.<\/li>\n<\/ul>\n\n\n\n
The augmentation stage is exactly what the whole RAG concept is built on. The model stops being a “general” tool and starts to act like an expert working with the company knowledge base.<\/p>\n\n\n\n
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<\/span>3. Generation – producing an answer based on context<\/span><\/h2>\n\n\n\nIn the final step the LLM generates an answer based on:<\/p>\n\n\n\n
\n- the user’s question,<\/li>\n\n\n\n
- the enriched context,<\/li>\n\n\n\n
- data retrieved in real time.<\/li>\n<\/ul>\n\n\n\n
As a result the model does not guess, it draws conclusions from real information<\/strong>. RAG also makes it possible to cite sources, summarize large documents, combine information from many places and eliminate errors caused by gaps in the model’s training.<\/p>\n\n\n\nThe user therefore receives an answer that is reliable, verified and tailored to the specifics of their organization.<\/p>\n\n\n\n
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<\/span>How does RAG solve the problems of large language models?<\/span><\/h1>\n\n\n\n<\/span>1. No up-to-date knowledge<\/span><\/h3>\n\n\n\nLLMs have no access to the internet or to new information after their training ends.
RAG pulls data on an ongoing basis from current documents and knowledge bases, so the answers match the actual state of affairs.<\/p>\n\n\n\n
<\/span>2. No specialist knowledge<\/span><\/h3>\n\n\n\nLanguage models handle natural language brilliantly, but they do not know internal company procedures.
RAG connects them with your own knowledge base – the model automatically becomes an expert in the field the organization works in.<\/p>\n\n\n\n
<\/span>3. Hallucinations<\/span><\/h3>\n\n\n\nLLMs sometimes generate answers that sound convincing but are untrue.
RAG significantly reduces this problem, because the answers are built on verified sources. If the information is missing, the model can safely reply that it has no knowledge on the subject.<\/p>\n\n\n\n
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<\/span>RAG AI – a breakthrough in information processing<\/span><\/h1>\n\n\n\nClassic language models such as GPT-4 or LLaMA are powerful tools, but their knowledge is static.
RAG introduces a dynamic component: the ability to use data in real time.<\/p>\n\n\n\n
As a result:<\/p>\n\n\n\n