RAG Testing and Evaluation for AI Pipelines

Your RAG demo works. Production is another story. We test RAG applications across the full pipeline to see what really happens when users ask messy, real questions, and whether the system retrieves the right knowledge.

Most RAG issues start in retrieval. Wrong chunk, wrong answer. We inspect embeddings, vector search behavior, and ranking logic to see why the system misses the right document and how to fix it.

RAG systems love to sound convincing. We run RAG system evaluation to check if answers actually come from the retrieved sources. If the model invents facts or blends documents incorrectly, we catch it early.

Prompt injection. Data leaks. Access to restricted documents. Our RAG security testing attacks your AI the way curious users and hackers would, so you can fix weak spots before launch.

We build a golden question set from real user queries, with verified answers and sources. An LLM-as-a-judge scores every answer for accuracy and grounding, and our QA engineers review the borderline cases.

Every update can break your RAG answers. Our RAG regression testing reruns the golden question set after each change, flags any drop in quality, and shows which questions broke before the release ships.

We check that the chunks your system retrieves are relevant and the most useful ones rank first, so the model isn’t distracted by noise.

We confirm retrieval returns everything needed to answer the question. Missing context is a common reason a RAG system gives half an answer.

Mean reciprocal rank shows how high the first relevant document appears in your results. If the best source sits too low, the model may never use it.

We verify every claim is backed by the retrieved context. Using RAG evaluation tools like Ragas and DeepEval, we catch hallucinations before users do.

We check that each answer addresses the question actually asked. It’s one of the core LLM evaluation metrics we apply to every RAG pipeline.

We confirm each cited source contains the claim it supports. In finance, healthcare, and legal products, a wrong citation can cost more than no citation.

We compare final answers against a golden question set with verified answers, so you know how often users get the right result.

We measure how long the full query takes, from retrieval to complete answer. A correct answer that arrives too slowly still loses users.

Support assistants answer thousands of questions daily. One wrong response can confuse users or overload support teams. A structured RAG assessment helps verify that answers come from the right documentation and stay consistent with your product policies.

Internal copilots rely on company documents, policies, and databases. If retrieval fails, employees get misleading answers. Testing ensures the RAG pipeline retrieves the right sources and uses them correctly across complex knowledge bases.

Finance, healthcare, and legal products must provide traceable, grounded answers. Teams rely on RAG evaluation metrics to prove responses are supported by trusted documents and meet internal quality and compliance expectations.

AI tools exposed to customers attract curious users and sometimes attackers. Validating RAG security helps ensure the system handles prompt injections, sensitive data, and restricted content safely before deployment.

Our QA team works daily with complex AI-driven products. We approach RAG testing like engineers, not theorists. Every RAG analysis focuses on how answers behave in real user scenarios, not just synthetic benchmarks.

Most AI teams debug prompts while the real issue sits in retrieval. We start with the foundation — RAG search quality. If the system retrieves the wrong sources, no prompt will fix the answer.

AI assistants often access internal documents, policies, and sensitive data. We test for prompt injection, data leaks, and unsafe responses — the risks that can quietly break RAG security in production.

We treat RAG systems like production software. Our engineers define measurable quality criteria, run repeatable tests, and deliver clear results your team can act on immediately.

We work closely with ML engineers, product leads, and CTOs. No long theory decks — just clear findings, reproducible tests, and practical recommendations your team can implement immediately.

RAG systems evolve quickly as data and prompts change. Our testing approach fits continuous delivery: structured test datasets, repeatable evaluation runs, and fast feedback loops your team can integrate into development.

AI products require more than functional testing. We validate model behavior, response quality, edge cases, and system interactions to ensure AI-driven features work reliably in real user scenarios.

AI Testing Services

Large language models can generate convincing but incorrect answers. Our QA engineers test prompts, responses, and grounding logic to detect hallucinations, broken flows, and unsafe outputs before users encounter them.

LLM Testing Services

AI systems often process sensitive data. We identify vulnerabilities such as prompt injection, data exposure risks, and API weaknesses to ensure your product remains secure in production environments.

Security Testing Services

Your RAG pipeline is only as good as the model behind it. We measure accuracy, hallucination rate, and safety against golden datasets, so you can compare models, prompts, and releases with numbers instead of gut feel.

LLM Evaluation Services

AI applications must handle heavy queries and large datasets. We evaluate response times, system stability, and scalability under realistic loads to ensure your product performs well as usage grows.

Performance Testing Services

For companies building AI products continuously, a dedicated QA team provides ongoing testing, release validation, and quality monitoring, helping teams maintain stable and reliable systems as features evolve.

Dedicated QA Team Services