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Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation
- Many generative AI use cases still struggle with Retrieval Augmented Generation (RAG) despite attempts to improve it with Agents.
- The Agentic Knowledge Distillation + Pyramid Search Approach simplifies the RAG process by focusing on distilling meaningful information.
- Using a pyramid structure, the approach involves converting documents to Markdown, extracting insights, distilling concepts, creating abstracts, and storing recollections.
- The pyramid structure allows for efficient retrieval in both traditional RAG and Agentic cases at inference time.
- Results from the approach show its effectiveness in fact-finding and complex research tasks, producing detailed reports with low token usage.
- Key benefits of the pyramid approach include reduced cognitive load, superior table processing, context preservation, optimized token usage, and efficient concept exploration.
- Challenges include establishing meaningful evaluation metrics, especially for nuanced questions and analytical responses.
- Future directions include tracking and evaluating recollections over time to ensure system success and applying the approach to organizational data for alignment purposes.
- The approach leverages the full power of Language Model (LLM) at ingestion and retrieval time, providing flexibility for various query types and promising performance for large datasets.
- Overall, the Agentic Knowledge Distillation + Pyramid Search Approach offers a significant improvement in response quality and performance for high-value questions.
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