3.7 Running experiments on models and pipelines

Key points

1. NVIDIA's chunking research tested strategies across several datasets. Even within the same document category, the best strategy varied significantly. So test chunking on your own content.

   What NVIDIA says (2)

   “Our research evaluated different chunking strategies across multiple datasets to establish guidelines for selecting the optimal approach based on your specific content and use case.”

   — Finding the Best Chunking Strategy for Accurate AI Responses

   “Even within the same document category, optimal chunking strategies varied significantly.”

   — Finding the Best Chunking Strategy for Accurate AI Responses

2. Standardized benchmarks give consistent datasets and metrics. NVIDIA warns that academic benchmarks can become saturated quickly as large language models (LLMs) improve, and tailored benchmarks for specific domains are often missing.

   What NVIDIA says (3)

   “Note that as LLMs develop quickly, academic benchmarks can become saturated quick”

   — Mastering LLM Techniques: Evaluation

   “The lack of tailored benchmarks for specific domains limits the relevance and depth of evaluations”

   — Mastering LLM Techniques: Evaluation

   “Standardized benchmarks provide consistent datasets and metrics to evaluate LLMs across a variety of tasks.”

   — Mastering LLM Techniques: Evaluation

Sample question

In NVIDIA's chunking experiments, what did they find about the best chunk size?