Evaluating RAG Applications Using AWS Bedrock Models: A Strategic Shift in Quality Engineering

Retrieval-Augmented Generation (RAG) is fast becoming one of the most widely used approaches in applied AI. At its core, RAG combines the power of large language models with external knowledge retrieval, allowing systems to generate more grounded, contextually aware responses. Whether it’s powering enterprise chatbots, document assistants, or intelligent search, RAG offers a way to blend generative fluency with factual accuracy.

But there’s a critical layer that often goes unnoticed, evaluation.

While teams obsess over improving retrievers and optimizing prompts, the quality engineering side of RAG has remained painfully underexplored. Are your responses truly faithful to the retrieved context? Do they reflect hidden bias? Can you prove completeness of the responses?

At Experion, we believe that testing is no longer an afterthought, it’s a strategic differentiator. That’s why we’ve built a modular, high-fidelity RAG evaluation framework that leverages AWS Bedrock-hosted models for scalable, secure, and intelligent QA. This blog explores how our approach bridges gaps in traditional testing and ushers in a new era of evaluation-led GenAI engineering.

The world of GenAI doesn’t play by the same rules as conventional software. In traditional systems, outputs are deterministic. You give an input, you get an expected response. Easy to test, easy to validate.

But RAG introduces nuance and unpredictability. The same query might yield different results depending on retrieval context or model variability. This dynamism exposes some very real challenges, ones we’ve seen across industries:

Evaluation processes often begin with good intentions but quickly hit bottlenecks. Review teams grow fatigued, annotation becomes inconsistent, and infrastructure costs spiral just to maintain parity across versions.

Worse still, quality metrics fluctuate as teams jump between tools or environments. One instance, the answers feel spot-on. The next, a few broken links or a subtle hallucination might go unnoticed, until it’s flagged by an end-user.

These aren’t just technical hiccups. They lead to erosion of trust, compliance gaps, and reputational risks. And they call for a fundamentally different approach.

When we began building our RAG evaluation framework, we started with a simple idea: evaluation isn’t a task. It’s a mindset.

Instead of checking boxes, we asked: What does a “good” answer really look like? How do we know a retrieved document is relevant? What if a response is factually correct, but fails to mention a crucial detail?
Answering those questions required more than metrics. It required an ecosystem.

We bake evaluation into the entire RAG lifecycle, from data prep to model outputs and everything in between. We started crafting test cases that mirrored real user behavior. We simulated edge cases, adversarial prompts, and ambiguous queries. And instead of running these checks once, we made them repeatable, traceable, and scalable.

As RAG applications grow in complexity, infrastructure can quickly become a barrier to effective evaluation, especially when teams rely on scattered tools and self-managed environments.

This is where cloud-native services like AWS Bedrock have emerged as game changers. With access to a growing set of foundation models like Claude, DeepSeek, Llama and more, evaluation can be carried out without the overhead of model hosting or GPU provisioning.

Just as importantly, Bedrock offers enterprise-grade security, with built-in IAM, encrypted storage, and audit-ready logging. These capabilities allow teams to scale evaluation pipelines quickly, while maintaining strict data governance and operational control.

By leveraging platforms like Bedrock, the focus shifts from infrastructure management to the more strategic task of designing intelligent, secure, and efficient evaluation workflows.

Behind the scenes, the Experion evaluation framework is made up of smart, modular components, each playing a specific role in the larger narrative of quality assurance.

It begins with the knowledge base, where source documents are vectorized and stored. This gives the RAG application its “memory”, but also defines the boundaries of what it should know.

Next comes the QA set generator, a tool that creates diverse question-answer pairs using prompt engineering and open-source components like DeepEval, among others. These test cases aren’t synthetic fluff. They reflect real user intentions, tricky phrasings, and domain-specific language.

Then there are the evaluator models, hosted on Bedrock, that take these inputs and assess the quality of generated responses. We use a blend of model-based scoring and rule-based checks, working with libraries like RAGAS, Giskard, and more.

Finally, the metrics layer ties it all together, measuring not just correctness, completeness, relevance, and safety, among others. And all of it is auditable, exportable, and ready to plug into CI/CD workflows.

Too often, evaluation is reduced to numbers on a spreadsheet. But in GenAI, metrics are narratives, they tell you where your system’s strengths lie and where it might fall short.

Some of the most telling metrics we use include:

• Faithfulness – Grounding of the answer in the retrieved context.
• Answer Relevance – Completeness and precision of the response.
• Context Relevance – Focus and usefulness of retrieved content.
• Bias and Toxicity – Inclusiveness and safety in output generation

These aren’t just academic checkmarks. They reflect user trust, business risk, and long-term sustainability.

And because every domain is different, be it banking, healthcare, or retail, our metrics layer adapts. You define what matters. We make sure it’s measured.

No evaluation framework is complete without facing its own stress tests. That’s why red teaming is baked into our process, not bolted on later.

We simulate prompt injections, fuzzing attacks, and malformed queries. We test for data poisoning and PII leaks. And we model worst-case scenarios using guidance from frameworks like the OWASP LLM Top 10.
The goal isn’t to break the system, it’s to build confidence that it can’t be broken easily.

What truly differentiates Experion’s approach is this: we don’t just evaluate RAG. We  productize evaluation.

Our framework integrates smoothly with your existing development pipelines, MLOps workflows, and compliance processes—without the need for complex configurations or lengthy setup cycles. Designed with flexibility in mind, it can support use cases across BFSI, healthcare, and retail, adapting to domain-specific needs with minimal customization. Built on open standards and reinforced for enterprise environments, it strikes the right balance between adaptability and robustness.

As GenAI applications mature, so too, the way we evaluate them. The evaluation process is no longer a standalone, manual step, it’s becoming a structured, intelligent discipline woven into the development lifecycle. Today’s frameworks benefit from tapping into a growing ecosystem of tools and libraries that support everything from contextual faithfulness checks to safety and completeness scoring.

For example, one of the open-source engines, like DeepEval is enabling more programmable, transparent approaches to evaluation. By defining test cases, applying model-driven metrics, and using structured prompts (like GEval), teams can assess LLM outputs based on reasoning, not just surface-level accuracy. When paired with scalable foundation models hosted on AWS Bedrock, such as Claude, DeepSeek, and others, this setup supports consistent and auditable evaluations, free from the inconsistencies of locally hosted environments.

That said, evaluation is rarely about a single method or library. The most effective frameworks are built to integrate and adapt, leveraging a combination of evaluators, metrics engines, and orchestration strategies that suit the application’s domain, maturity, and scale. Whether incorporating components like RAGAS, Giskard, or custom scoring layers, the goal remains the same: to build trust through transparency, repeatability, and depth.

This multi-layered approach ensures that evaluation keeps pace with the evolving complexity of RAG systems, remaining flexible, scalable, and aligned with real-world quality goals.

In today’s GenAI landscape, most organizations approach evaluation as a checkbox, a necessary step tacked on after development, often constrained by tooling or team bandwidth. At Experion, we challenge that mindset. We treat evaluation not as a chore, but as a product in itself, designed, engineered, and continuously refined to deliver lasting value.

What sets our RAG Evaluation Framework apart isn’t just technical integration, it’s the philosophy behind it.

We’ve created a hybrid ecosystem that brings together the best of both worlds: the flexibility of open-source tools like DeepEval, RAGAS, and Giskard, combined with the stability and scale of AWS Bedrock. This allows teams to start small and scale fast, without being locked into rigid infrastructure or vendor-specific workflows.

More importantly, we go beyond surface-level correctness. Our framework evaluates for safety, fairness, robustness, and contextual fidelity, offering full-spectrum testing that aligns with real-world risks, not just model benchmarks.

From banking and insurance to healthcare and retail, we’ve developed domain-aware evaluation testbeds that account for industry-specific considerations, such as regulatory requirements, data sensitivity, and contextual complexity. This built-in adaptability helps ensure that our framework aligns with enterprise needs while remaining practical and resilient across varied use cases.

Explore our GitHub repository – a lean version of our repository to see the architecture in action.

There’s no shortage of innovation in the RAG space. Proofs of concept are everywhere. But the line between a flashy demo and a scalable, trusted solution lies in one critical layer: evaluation.

Too many GenAI failures are not due to weak models, but due to blind spots in testing. Silent hallucinations, biased responses, retrieval gaps, these issues go undetected without a robust, thoughtful evaluation strategy.

That’s why Experion’s RAG Evaluation Framework is more than a toolkit, it’s a strategic foundation for responsible AI adoption. It enables organizations to move from experimentation to production with the assurance that every answer is traceable, grounded, and safe.

We help teams quantify quality rather than guess it, operationalize trust rather than assume it, and embed compliance and governance from the first iteration, not the final audit.

Retrieval-Augmented Generation is powerful. But only when you can prove that it works, and works safely. That’s where we come in.

If you’re building enterprise GenAI applications and want to ensure they’re not only intelligent, but trustworthy, scalable, and aligned with business risk, Experion is ready to partner with you.

Talk to our QA and AI/ML Strategy Team to explore how.