Working with AI to Create a Retrieval-Augmented Generation (RAG) System

  • Client: Open AI Research
  • Type: AI / Machine Learning
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Retrieval-Augmented Generation (RAG) enhances AI by retrieving relevant external data at query time, enabling grounded, domain-specific answers.

🧠 What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that enriches large language models (LLMs) by integrating external knowledge retrieval during response generation.
Instead of relying solely on pre-training, RAG pulls in up-to-date and domain-specific data for more accurate results.
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RAG Architecture

⚙ How RAG Works

  1. Indexing / Embeddings
    Break documents into chunks and convert them into vector embeddings stored in a vector database.

  2. Retrieval
    Convert a user’s query into an embedding and retrieve the most relevant chunks from the database.

  3. Augmentation
    Insert retrieved chunks into the LLM prompt to provide context.

  4. Generation
    The LLM generates an answer grounded in both its own knowledge and the retrieved data.

RAG Pipeline Diagram

✅ Benefits of RAG

  • Better Accuracy & Less Hallucination – Uses grounded facts.
  • Up-to-Date Knowledge – Fetches recent info without retraining.
  • Source Attribution – Can cite references for transparency.
  • Cost Efficiency – Avoids expensive fine-tuning.

⚠ Challenges

  • Misinterpretation of retrieved context.
  • Quality of results depends on data quality.
  • Static retrieval may not adapt to mid-generation needs.