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rag fundamentals

🔹 what is rag?

retrieval-augmented generation (rag) is a technique that combines information retrieval with text generation to improve the quality and accuracy of ai responses.

instead of relying solely on pre-trained knowledge, rag systems:

  • retrieve relevant information from external sources
  • augment the input with this retrieved context
  • generate responses based on both the query and retrieved information

🔹 how rag works

  1. query processing: user asks a question
  2. retrieval: system searches relevant documents/knowledge base
  3. augmentation: retrieved information is added to the query
  4. generation: llm generates response using both query and context
  5. response: user receives accurate, contextual answer

🔹 rag components

retrieval system:

  • vector databases (pinecone, weaviate, chroma)
  • embedding models (openai, sentence-transformers)
  • similarity search algorithms

generation system:

  • large language models (gpt, claude, llama)
  • prompt engineering techniques
  • context window management

integration layer:

  • api orchestration
  • response formatting
  • error handling and fallbacks