Beyond RAG: The Future of AI Context with CHORUS
AI is moving fast, but one of the biggest bottlenecks isn't model size or compute power—it's context management. Here's how CHORUS goes beyond traditional RAG approaches.
Anthony Rawlins
CEO & Founder, CHORUS Services
AI is moving fast, but one of the biggest bottlenecks isn’t model size or compute power, it’s context management.
For years, Retrieval-Augmented Generation (RAG) has been the go-to method for extending large language models (LLMs). By bolting on vector databases and search, RAG helps models pull in relevant documents. It works, but only to a point. Anyone who’s scaled production systems knows the cracks:
- RAG treats knowledge as flat text snippets, missing relationships and nuance.
- Git and other version-control systems capture code history, but not the evolving reasoning behind decisions.
- Static context caches snap a picture in time, but knowledge and workflows don’t stand still.
In short: RAG, Git, and static context snapshots aren’t enough for the next generation of AI.
Why Hierarchical Context Matters
Knowledge isn’t just a pile of files — it’s layered, temporal, and deeply interconnected. AI systems need to track how reasoning unfolds, why decisions were made, and how context evolves over time. That’s where Chorus comes in.
Instead of treating context as documents to fetch, we treat it as a living, distributed hierarchy. Chorus enables agents to share, navigate, and build on structured threads of reasoning across domains and time. It’s not just about retrieval — it’s about orchestration, memory, and continuity.
Research Is Moving the Same Way
The AI research frontier points in this direction too:
- NVIDIA’s recent small model papers showed that scaling up isn’t the only answer — well-designed small models can outperform by being more structured and specialized.
- The Hierarchical Reasoning Model (HRM) highlights how smarter architectures, not just bigger context windows, unlock deeper reasoning.
Both emphasize the same principle: intelligence comes from structure, not size alone.
What’s Next
Chorus is building the scaffolding for this new paradigm. Our goal is to make context:
- Persistent – reasoning doesn’t vanish when the session ends.
- Navigable – past decisions and justifications are always accessible.
- Collaborative – multiple agents can share and evolve context together.
We’re not giving away the full blueprint yet, but if you’re interested in what lies beyond RAG, beyond Git, and beyond static memory hacks, keep watching.
The future of AI context management is closer than you think.