SAN FRANCISCO, Nov. 04, 2025 (GLOBE NEWSWIRE) -- RapidFire AI, the company accelerating AI experimentation and customization, today announced at Ray Summit 2025 RapidFire AI RAG, an open-source extension of its hyperparallel experimentation framework that brings dynamic control, real-time comparison, and automatic optimization to Retrieval-Augmented Generation (RAG) and context engineering workflows.

Agentic RAG pipelines that combine data retrieval with LLM reasoning and generation are now at the heart of enterprise AI applications. Yet, most teams still explore them sequentially: testing one chunking strategy, one retrieval scheme, or one prompt variant at a time. This leads to slow iteration, expensive token usage, and brittle outcomes.

“Throwing more GPUs at LLM fine-tuning and multi-model experiments is a hit-or-miss approach to enterprise AI development,” said Kirk Borne, Founder, Data Leadership Group. “The future belongs to teams that perform systematic experimentation — understanding how retrieval, chunking, and prompt design interact to shape model performance. RapidFire AI RAG exemplifies this shift with smart GPU utilization, intelligent experiment parallelization, real-time monitoring with live interaction, and precision-tuned model optimization to deliver measurable results faster."

That experimental discipline is what separates successful deployments from stalled proofs of concept. According to Arun Kumar, Cofounder and Chief Technology Officer at RapidFire AI. “Teams often assume RAG will ‘just work’ once their data is chunked and indexed. But one size never fits all, every chunking scheme, retrieval and reranking scheme, and prompt structure interacts differently. RapidFire AI RAG brings the same empirical rigor and acceleration power that we pioneered for fine-tuning and post-training to RAG and context engineering pipelines.”

Hyperparallel RAG Experimentation

RapidFire AI RAG applies the company’s hyperparallel execution engine to the full RAG stack, allowing users to launch and monitor multiple variations of data chunking, retrieval, reranking, prompting, and agentic workflow structure simultaneously, even on a single machine. Users see live performance metrics update shard-by-shard, can stop or clone runs mid-flight, and inject new variations without rebuilding or relaunching entire pipelines. Under the hood, RapidFire AI intelligently apportions token usage limits (for closed model APIs) and/or GPU resources (for self-hosted open models) across these configurations.

“In enterprise AI, the hard part isn’t building the pipeline—it’s knowing which combination of retrieval, chunking, and prompts actually delivers trustworthy answers,” said Madison May, CTO of Indico Data. “RapidFire AI gives teams the structure to test those assumptions quickly and see what really works, instead of relying on intuition or luck.”

Dynamic Control and Automated Optimization

Beyond parallel exploration, RapidFire AI RAG introduces dynamic experiment control, a cockpit-style interface to steer runs in real time, and a forthcoming automation layer that supports AutoML algorithms and customizable automation templates beyond just grid search or random search to optimize holistically based on both time and cost constraints.

Maximal Generality and Open Integration

Unlike closed-system RAG builders tied to specific clouds or APIs, RapidFire AI RAG supports hybrid pipelines that mix self-hosted models and closed model APIs across embedding, retrieval, re-ranking, and generation steps. Users can run with OpenAI or Anthropic models, Hugging Face embedders, self-hosted rerankers, and any vector/SQL/full-text search backend, all within the same experiment workspace.

“We’re opening a new era for RAG and context engineering where organizations can truly measure, compare, and optimize their data pipelines instead of treating them as black boxes,” said Jack Norris, Cofounder and CEO of RapidFire AI. “As applications get more domain-specific, experimentation and control, not just access to data, will define success.”

RapidFire AI’s technology is rooted in award-winning research by its Co-founder, Professor Arun Kumar, a faculty member in both the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego.

Availability

RapidFire AI RAG is available now as part of the company’s open-source release and installable via pip install rapidfireai.

To learn more, visit rapidfire.ai or explore the open-source repository on GitHub and the documentation site.

About RapidFire AI

RapidFire AI is an open‑source system for accelerating AI customization, spanning fine‑tuning, post‑training, agentic RAG, and context engineering. RapidFire AI lets users run, compare, and dynamically control multiple configurations in real time. Backed by .406 Ventures, AI Fund, Osage University Partners, and Willowtree Investments, RapidFire AI helps teams iterate faster, maximize compute efficiency, and deliver better AI results with less friction.

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