Answer extracted from The Josh Bersin Company podcast — listen to the full episode below.
How Much More Efficient Is the Agent Ready Corpus Than ChatGPT or Claude?
The Agent Ready Corpus is 10 to 100 times more efficient at token usage for answering complex HR questions than general-purpose models like ChatGPT or Claude. While those models hallucinate and misattribute information when handling nuanced human capital scenarios, the optimized ARC delivers accurate, predictable, and contextually relevant answers grounded in real case studies and benchmarks.
Precision Over Generalization: The Core Difference
General-purpose language models lack the deep, structured knowledge required to answer complex HR and management questions accurately. When posed with a question about performance management practices in a mid-sized tech company in the UK, ChatGPT or Claude will often either invent plausible-sounding answers or conflate unrelated best practices, because they have no reliable way to distinguish between what applies to Google, a small retailer, or a fast-growing startup.
The Agent Ready Corpus solves this by indexing and tagging research content across eight distinct dimensions—industry, company size, geography, functional capability, and others—so the language model can retrieve and reference the exact case studies and benchmarks that apply to the user's specific scenario. This structured approach eliminates hallucination and ensures every answer is backed by real evidence.
Benchmarking Against Real-World Complexity
The efficiency gain became measurable through rigorous testing. Bersin's team benchmarked the ARC against general LLMs using 30 golden prompts—complex, multifaceted HR questions drawn from real advisory work. The results were decisive: token usage dropped dramatically while accuracy, reliability, and contextual relevance all improved. As explained in detail in the podcast episode, the ARC architecture allows the model to "read extremely well and does not have to hallucinate."
"We essentially built a new architecture that takes the essence of Galileo and carefully indexed it and tagged it in eight different dimensions so that the LLM can read it extremely well and does not have to hallucinate."
Josh Bersin — Founder and CEO of The Josh Bersin Company. For nearly 30 years, Bersin has studied human capital practices across management, leadership, and HR, identifying 95 functional capability areas and developing nearly 1,800 case studies. His research spans practices and benchmarks from approximately 120 countries, making him one of the most comprehensive sources of actionable HR intelligence grounded in comparative organizational data.
This specificity matters enormously for practical use. A CFO in Japan asking about pay equity practices for a distribution company of 5,000 employees receives an answer that references similar organizations in similar markets, not a generic summary that conflates disparate contexts. The ARC's efficiency gain isn't just about fewer tokens—it's about eliminating wasted computation on uncertainty and hallucination.
The practical outcome is also speed and cost reduction: the Jupiter Release, as discussed in this episode, makes it viable to run Galileo as a portable intelligence layer across multiple platforms—integrated into ChatGPT, Claude, Microsoft Copilot, or Google Gemini—without the infrastructure burden or latency penalties that would come from relying on those models' raw capability alone.
See also
What structural changes were made to optimize research content for AI consumption, and what is the Agent Ready Corpus?
The Josh Bersin Company restructured their research content—traditionally written like books with introductions and conclusions—into a tagged, indexed corpus optimized for language models to read and retrieve information efficiently without hallucination.
Why did the strategy shift from building a specialized AI agent to creating a portable intelligence layer across multiple platforms?
Customers indicated they already had AI platforms like ChatGPT, Gemini, Claude, or Microsoft Copilot and wanted Galileo integrated there rather than as a standalone agent they'd have to adopt separately.
What opportunity did the emergence of large language models like ChatGPT create for HR research organizations?
When ChatGPT came out, Bersin saw an opportunity to build a search engine where users could query an entire corpus of knowledge and get answers, then find supporting case studies and research to validate and contextualize those answers.