
How AI Is Built
#041 Context Engineering, How Knowledge Graphs Help LLMs Reason
Feb 6, 2025
Robert Caulk, who leads Emergent Methods and has over 1,000 academic citations, dives into the fascinating world of knowledge graphs and their integration with large language models (LLMs). He discusses how these graphs help AI systems connect complex data relationships, enhancing reasoning accuracy. The conversation also touches on the challenges of multilingual entity extraction and the need for context engineering to improve AI-generated content. Additionally, Caulk shares insights into upcoming features for real-time event tracking and the future of project management tools.
01:33:35
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Quick takeaways
- Context engineering optimizes LLM performance by curating input signals to enhance outputs while eliminating distracting noise.
- Structured data presentation, like bullet points and tables, significantly improves an LLM's ability to efficiently process complex information.
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The Importance of Context Engineering
Context engineering is critical for optimizing the performance of large language models (LLMs). It involves curating input signals to ensure that every piece of context functions as a feature that informs the model's output while eliminating noise and distractions that might sidetrack it. Achieving a high signal-to-noise ratio leads to clearer inputs, which in turn generates better outputs. This process mirrors traditional machine learning feature engineering, highlighting the significance of structured, meaningful representations of raw data.
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