Corrective Retrieval-Augmented Generation (RAG) with Dynamic Adjustments

Corrective Retrieval-Augmented Generation (RAG) enhances response accuracy by dynamically adjusting the retrieval process, ensuring relevant, up-to-date information.

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Project Outcomes

  • Built a CRAG system for query processing.

  • Enabled document retrieval with Chroma and FAISS.

  • Evaluated relevance using GPT-4o.

  • Adjusted responses with web searches when needed.

  • Generated sourced, concise answers.

  • Handled queries like image recognition.

  • Integrated LangChain for workflow efficiency.

  • Refined knowledge from documents and web.

  • Showcased NLP application potential.

  • Created a scalable AI research tool.

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