Caveman: Slashing AI Development Time on Complex Benchmarks

Running comprehensive AI benchmarks can drain both time and budget. A procedural world generation benchmark that requires building everything from scratch - no pre-made assets, no existing tools - typically consumes over an hour per run. At that pace, developers can squeeze in maybe one or two benchmark iterations per day before token costs spiral out of control. This bottleneck makes iterative development painfully slow.

Caveman, available at https://github.com/JuliusBrussee/caveman, addresses this exact problem by optimizing how language models handle complex, multi-step tasks without sacrificing output quality.

How Caveman Accelerates Model Performance

Caveman operates as a middleware layer that restructures how prompts get processed during extended generation tasks. Rather than treating each step in a complex workflow as an isolated interaction, it maintains context more efficiently across the entire task chain.

The tool achieved a 6x speedup on a procedural generation benchmark - reducing runtime from over 60 minutes to just 11 minutes. Token consumption dropped by 50% during the same test. The output remained identical to standard processing, suggesting Caveman optimizes the path to the solution rather than compromising on quality.

This performance gain matters most for benchmarks involving multiple interconnected steps. Tasks like procedural world generation require the model to maintain consistency across terrain generation, object placement, physics calculations, and rendering logic. Traditional approaches often involve redundant context passing and repeated instructions across each phase.

Setting Up Caveman

Installation follows standard Python package patterns. The repository includes setup instructions and dependencies in the README file. Developers working with OpenAI’s API or compatible endpoints can integrate Caveman into existing workflows with minimal code changes.

The tool works by wrapping API calls rather than requiring model fine-tuning or specialized infrastructure. This means teams can test it on current projects without rebuilding their entire pipeline.

Configuration involves specifying the task structure and defining how subtasks relate to each other. For procedural generation workflows, this might mean identifying which steps depend on previous outputs versus which can be processed more independently.

Applying Caveman to Real Workflows

The procedural world generation test provides a concrete example. The benchmark at https://darkounity.com/unity-ai requires generating an entire game environment from scratch - terrain, objects, lighting, and interactive elements - purely through AI-generated code and logic.

Without optimization, this task involves:

• Initial world structure generation
• Terrain detail passes
• Object placement algorithms
• Physics and collision setup
• Rendering and optimization

Each phase traditionally requires full context from previous steps, leading to exponentially growing token usage. Caveman identifies which context elements remain relevant across phases and which can be compressed or referenced rather than repeated.

The 50% token reduction translates directly to cost savings. For teams running dozens of benchmark iterations during development, this optimization can shift a project from prohibitively expensive to economically viable.

Code integration looks something like:

result = optimize_task(
 task_description="Generate procedural world",
 subtasks=["terrain", "objects", "physics"],
 model="gpt-4"
)

The actual implementation handles context management automatically based on task structure.

When Caveman Falls Short

The tool shows the most dramatic improvements on multi-step tasks with clear dependencies. Single-shot generation or simple question-answering won’t see the same benefits since there’s minimal context to optimize.

Tasks requiring extensive back-and-forth iteration may not fit Caveman’s current architecture as cleanly. The tool works best when the overall workflow can be defined upfront rather than discovered through interactive exploration.

Token savings depend heavily on task structure. Highly interconnected tasks where every step genuinely needs full context from all previous steps will see smaller gains than workflows with more modular phases.

The project remains under active development, which means APIs and configuration approaches may evolve. Teams building production systems should monitor the repository for breaking changes.

For developers frustrated by benchmark bottlenecks or token costs on complex generation tasks, Caveman offers a practical solution worth testing. The combination of dramatic speed improvements and cost reduction makes it particularly valuable for iterative development workflows where running multiple tests per day determines project velocity.