Guest: Arun Kumar (LinkedIn)
Company: RapidFire AI
Show Name: An Eye on AI
Topic: AI Infrastructure

Retrieval-Augmented Generation (RAG) has quickly become a foundation for modern AI applications — but too often, it’s treated like a black box. Developers assume it will “just work,” only to run into slow performance, high costs, and inconsistent results. Arun Kumar, CTO and Co-Founder of RapidFire AI, joined me to explain how his company’s new open source framework, RapidFire AI RAG, is reimagining how teams experiment, customize, and optimize RAG workflows for real-world success.

Bridging the Gap Between Models and Use Cases

“Open models like Llama and Mistral are powerful,” Kumar explained, “but they don’t understand your specific task data or evaluation metrics.” That’s where RapidFire AI comes in — to bridge the gap between generic model capabilities and application-specific needs.

The RapidFire AI platform provides a system for customizing large language models (LLMs) faster, cheaper, and with greater precision. Instead of spending weeks fine-tuning models manually, users can run automated experiments across multiple configurations simultaneously. This flexibility enables developers to adapt models to their own data, infrastructure, and use cases.

Bringing Hyperparallel Experimentation to RAG

At the heart of the new RapidFire AI RAG release is what Kumar calls “hyperparallel experimentation.” In practical terms, this means developers can run dozens of configuration variations — across chunking, retrieval, embedding, and ranking — at once, even on a small machine with just a few GPUs.

Traditionally, AI teams might run one training or tuning job at a time, adjusting hyperparameters and waiting hours or days to see the impact. With hyperparallel execution, those iterations happen in real time. “You can compare 16 or more configurations simultaneously,” said Kumar. “And you can dynamically stop unproductive runs in flight, saving both GPU cycles and token costs.”

This real-time control marks a shift from the passive monitoring dashboards common in AI tooling today. RapidFire AI turns experimentation into an interactive process — a concept borrowed from high-performance computing and applied to modern AI workflows.

Why Chunking and Context Engineering Matter

A central theme in the conversation was the concept of context engineering — the practice of controlling what the model “sees” during inference. Since many LLMs are frozen or hidden behind APIs, developers can’t change the model itself. What they can change is the context: how data is chunked, embedded, retrieved, and re-ranked before it reaches the model.

Most teams underestimate how much this matters. “People often assume one chunking strategy will work for everything,” Kumar said. “But the right approach depends entirely on the query and the use case.” Chunking at a paragraph level might split related information; chunking at a sentence level might dilute context; and using entire documents can blow up token costs.

RapidFire AI RAG makes it easier to test these variations systematically — not by guesswork, but by experimentation and metrics-driven comparison. In other words, it turns RAG tuning into a science, not an art.

Dynamic Control and Automation

Another powerful innovation in RapidFire AI RAG is dynamic experiment control. From a single dashboard, users can pause, clone, and modify experiments on the fly — even while they’re running.

“If I’ve launched 16 configurations, I can prune the ones that aren’t working and inject new variations based on what I’m seeing,” Kumar explained. “I don’t have to start over or manually reconfigure clusters.”

This automation not only saves time but also improves resource efficiency. Whether using GPUs for open models or tokens for closed APIs, developers can make real-time trade-offs between cost, accuracy, and latency. For enterprises working under budget constraints, that flexibility is crucial.

Open Source and Interoperability

RapidFire AI’s open source approach is central to its mission. Kumar, who is also a professor at UC San Diego, emphasized the importance of transparency and collaboration in AI development.

“In academia, we love open source — but in industry, there are trade-offs,” he said. “We want to build on open ecosystems while still supporting enterprises that need reliability and service-level guarantees.”

That’s why RapidFire AI RAG supports both self-hosted and proprietary integrations, including APIs from OpenAI and Google. The company plans to offer both community-driven open source releases and supported enterprise versions for customers who need additional security or performance assurances.

Preparing Enterprises for Agentic AI

Looking ahead, Kumar sees RAG and context engineering as key stepping stones to more autonomous, agentic AI systems — models that can act on information and make decisions independently. But as enterprises scale up experimentation, governance and flexibility become just as important as innovation.

“Enterprises fear lock-in,” Kumar said. “They want control and the ability to swap vendors or models easily. Our framework supports that by letting you mix open and closed models, fine-tuning or inference workflows, all within the same stack.”

This adaptability extends across what RapidFire AI calls the “customization spectrum” — from prompt engineering to retrieval workflows, fine-tuning, and continued pre-training. The same engine powers each stage, so teams can evolve their AI strategy without switching tools.

Experimentation as the New Foundation of AI

The message behind RapidFire AI RAG is clear: in the next phase of enterprise AI, experimentation will define success. Models are no longer the only differentiator; the ability to test, adapt, and automate faster than competitors will determine who wins.

“Go try it yourself,” Kumar concluded. “Run it on your data. You’ll see the benefits.”