Rapid Reads — Maximizing LLM Performance: Smart Data Exposure
In this article, we delve into the impact of data exposure on prompting changes within an LLM. When unnecessary data pipelines are eliminated, it streamlines model complexity effectively.
So, you’ve just deployed your first application with an LLM. Chances are, you’re encountering one of two issues: inconsistency in model performance or significant latency in responses. What’s the next step?
Accuracy?
To address consistency concerns, focus on overall model tuning. Experiment with temperature settings, which act as creativity thresholds for most models, or explore different prompting strategies to gauge which yields the most consistent results.
Handling latency with smart data exposure
However, when latency becomes the primary concern, a plethora of potential solutions arises. But how do you determine the most effective approach?
Rather than resorting to conventional methods, consider the type of data being exposed to the LLM. For instance, imagine you’re utilizing a RAG model accessing data from a complex database. Constantly querying such intricate data through the LLM introduces significant latency challenges. While internal optimization and fine-tuning are options, they fail to address the core issue. Contrary to LLM beliefs, sometimes the problem lies in the data itself.
A straightforward solution involves maintaining contextual data summaries to enhance response speed while preserving knowledge. By pre-loading data splits and priming the LLM accordingly, the need for repeated querying diminishes, reducing the burden on sub-layers engaged in querying processes.
Now, you might ask, “Won’t this lead to memory overload?”
The answer is nuanced. Memory overload varies depending on the application’s demands and constraints. If minimizing latency and maximizing accuracy are top priorities, as they should be for any conversational AI system, consider experimenting with this approach.
Share your experiences and insights gained from implementing this strategy.
