A config-driven platform to systematically compare reasoning strategies like Naive Prompting, Chain-of-Thought, RAG, and ReAct Agents with comprehensive metrics tracking.
Designed to deeply understand the trade-offs in accuracy, latency, and tokens across different AI architectures.
Run head-to-head tests between standard prompting, Chain-of-Thought, RAG pipelines, and ReAct agent workflows.
Track model accuracy, performance latency, exact matches, token-level F1 scores, and inference cost natively.
Submit experiments matching datasets to models. Background queues handle API failures and batch processing reliably.
Designed using React 19, Next.js 16, and FastAPI. Zero database locks on long generations thanks to asyncio threading.
Examine how knowledge graph injection and dense vector retrieval modify response accuracy compared to zero-shot approaches.
Implement custom moderation prompts to test boundaries safely, comparing how strictly different LLMs adhere to system instructions.
Don't guess what works best. Forge clear hypotheses and run direct technical comparisons. Uncover the exact latency overhead of Chain-of-Thought versus the accuracy gain it yields on complex benchmarks.
