The Token Tax of Epistemic Accuracy: Comparing RAG and Long-Context Architectures for Document-Grounded Generative AI Applications

2026-06-18

AI Summary

This study addresses the critical challenge of balancing generation accuracy against computational cost in high-stakes, knowledge-intensive tasks. It systematically compares two document-grounding approaches—Retrieval-Augmented Generation (RAG) and long-context prompting—within the context of manufacturing safety training, evaluating their performance trade-offs. The work introduces two metrics, “cognitive accuracy” and “token tax,” to quantify the benefits and costs of broader evidence access. Experimental results across expert-validated benchmarks, multiple language models, and diverse environments show that long-context prompting achieves a cognitive accuracy of 73.1%, significantly outperforming semantic RAG at 65.4%, yet incurs a 26-fold increase in token consumption per query. These findings reveal a pronounced tension between accuracy and computational efficiency in grounded generation.

Abstract

Document-grounded assistants built on large language models are increasingly used in high-stakes, knowledge-intensive work. Their usefulness, however, may depend on how evidence is allocated before generation. We investigate such a claim by comparing two grounding architectures: (a) retrieval-augmented generation (RAG) that retrieves a few relevant passages, and (b) long-context prompting, which loads the whole document collection in context. We view these as two regimes of "epistemic access" on an accuracy--cost frontier. We use "epistemic accuracy" to capture model correctness that depends on having the right evidence. We posit that broader access (via long context) can increase it, but with a "token tax" (i.e., a substantial increase in cost due to larger input token consumption). We probe this framing with a case study in manufacturing safety training. Using an expert-validated benchmark, we evaluate 972 answers across three machines, two small language models, and three retrieval/in-context prompting approaches. Long-context prompting achieved the highest correctness (73.1% vs. 65.4% for semantic RAG), but at 26 times the per-query token cost. We interpret this gap as the token tax of broader evidentiary access. We carefully discuss the implications of our findings for resource-constrained organizations.

Problem

• epistemic accuracy
• retrieval-augmented generation
• long-context prompting
• token cost
• document-grounded generation

Innovation

• epistemic accuracy
• token tax
• retrieval-augmented generation
• long-context prompting
• document-grounded generation