LLMs, Prompts, Agents, and the Rise of Graphs
Summary
This content discusses the evolution of interacting with Large Language Models (LLMs), moving from simple prompting to more complex agentic systems and ultimately to 'graphs'. The core argument is that while prompting, agents, and other intermediate steps are transient, the underlying 'graph' structure represents the enduring paradigm for organizing and running LLMs. The piece emphasizes that focusing solely on perfecting prompting techniques is a mistake, as the future lies in understanding and engineering these graph structures, which allow for more sophisticated and persistent system behaviors beyond sequential steps.
Key Insights
The 'graph' is the final and enduring form of LLM system architecture.
The 'graph' represents the layer where LLM systems stop being simple sequences of steps. It is where the system's persistent structure and capabilities reside, distinguishing it from transient methods like prompting.
Shape, not model or tokens, defines LLM system behavior.
The underlying language model and the tokens it processes remain constant. The crucial difference in how an LLM system operates comes from the 'shape' or structure in which it is run, pointing to the importance of graph engineering.
Graph engineering is the fundamental layer for advanced LLM systems.
The 'graph' is presented as the ultimate layer in LLM development, implying that all previous methods like prompting and agents are merely steps leading to this more robust and fundamental structure. Building these graphs is key.
Sections
The Evolution of LLM Interaction
Prompting is a temporary technique in LLM interaction.
The current focus on perfecting prompt engineering is seen as a fading technique. The speaker suggests that everything else besides the 'graph' is a step that will eventually be superseded.
The progression: LLMs lead to Prompts, then Agents, then Graphs.
The development path for interacting with LLMs follows a sequence: Large Language Models (LLMs) enable Prompts, which in turn allow for the creation of Agents. Ultimately, these Agents evolve into or are built upon a more fundamental 'Graph' structure.
The 'graph' is the final and enduring form of LLM system architecture.
The 'graph' represents the layer where LLM systems stop being simple sequences of steps. It is where the system's persistent structure and capabilities reside, distinguishing it from transient methods like prompting.
Focusing on prompts neglects the future of LLM systems.
Many people are currently investing effort into perfecting prompt engineering, which is described as 'the thing that goes away first'. This focus is misdirected as the more significant development lies beyond prompting.
Shape, not model or tokens, defines LLM system behavior.
The underlying language model and the tokens it processes remain constant. The crucial difference in how an LLM system operates comes from the 'shape' or structure in which it is run, pointing to the importance of graph engineering.
Graph Engineering as the Future
Graph engineering is the fundamental layer for advanced LLM systems.
The 'graph' is presented as the ultimate layer in LLM development, implying that all previous methods like prompting and agents are merely steps leading to this more robust and fundamental structure. Building these graphs is key.
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