Large enterprise systems don’t look like TMMI tutorials. They look like KYC flows with edge cases, ICD codes in clinical records, and ten years of “temporary” workarounds.
And then LLMs are added to “make things more efficient.”
So, you get a system that doesn’t know your business rules, can’t trace its decisions, ignores your risk model, but sounds confident when hallucinating.
To address this, mid-sized and enterprise-level companies have begun implementing RAG (Retrieval-Augmented Generation) systems. They connect powerful language models and the industry’s unique knowledge base.
The Retrieval-Augmented Generation (RAG) market is growing with an impressive CAGR of approximately 49.1% projected from 2025 to 2030. The market size is expected to increase from around USD 1.2 billion in 2024 to about USD 11.0 billion by 2030.
In this article, I want to talk about why it happens, and what QA teams should do about it now (or, better say, yesterday).
Why Enterprises Move from LLM to RAG+LLM
Generic LLMs are powerful but lack context about your specific business. They don’t know your internal policies, can’t reference your historical incidents, and have no access to your compliance requirements.
This gap between general AI capabilities and enterprise-specific needs is why we are talking about RAG (Retrieval-Augmented Generation) systems.
Hallucinations vs. grounding
LLMs without context invent details. RAG forces answers to rely on your requirements, policies, risk catalog, and incidents — with citations. Less guesswork, more evidence.
Traceability for audit
In QA we need more than “a sensible test.” We need why a test belongs, which control it covers, and which policy version it maps to. RAG gives a paper trail.
Private data and compliance
RAG works on private sources and limits what is exposed in answers. Easier to align with PCI DSS, SOX, HIPAA.
Vertical, not generic
“Vertical” means domain language and rules: transaction types, medical codes, claims logic, control catalogs. This reduces noise and false positives in test design.
Governance rails exist
You don’t have to invent controls from scratch. NIST’s AI RMF + Generative AI Profile and ISO/IEC 42001 provide clear tasks, roles, and evidence patterns to run GenAI responsibly at enterprise scale.
RAG adds a retrieval step so the model uses your internal sources — requirements, risk registers, policies, incident post-mortems, and historic defects. Answers come with citations to those sources.
Black-box answers do not pass audits.
A QA Action Plan for LLM+RAG
As a software testing company targeting AI, TestFort builds practical QA for LLM+RAG — from retrieval checks to guardrails. An internal team can do the same.
#1. Curate the sources of truth
- Gather current requirements, test cases, controls, policies, risk register, and incident post-mortems;
- Add metadata: owner, module/system, version, effective date;
- Automate re-indexing on policy or code changes. Stale content breaks “grounded” answers.
#2. Design RAG flows for testing
- Risk-based regression selection with a clear “why” per included test;
- Regulatory test design mapped to controls (PCI/SOX/HIPAA), showing explicit gaps;
- Defect triage & duplicates: normalize language, link similar incidents, recommend next steps;
- Synthetic data: realistic, domain-faithful, privacy-safe (no PII/PHI).
#3. Build governance from day one
- Treat prompts, embeddings, retriever scope like code: owners, reviews, change logs;
- Map controls to NIST GenAI Profile tasks and run the program under ISO/IEC 42001;
- For payments, plan according to the PCI DSS timelines and scope-reduction practices.
#4. Measure value with QA metrics, not demo wow
- Time for test design / regression;
- Coverage of high-risk requirements at a fixed test budget;
- Escaped defects (UAT/production);
- Retrieval precision/recall and groundedness; human trust ratings.
Testing RAG at Three Levels of Depth
When testing RAG, you need to validate retrieval accuracy, context selection, and generation quality — separately and together.
We’ve developed a three-level framework that catches issues from component bugs to system-wide failures.
Start with Level 1 and progress based on your risk tolerance and compliance requirements.
Level 1: Component Testing
Verify that the system finds the right documents when queried.
Retrieval testing
- Precision: Are retrieved docs relevant?
- Recall: Are all relevant docs retrieved?
- Ranking: Are best docs ranked first?
- Latency: Is retrieval fast enough?
Test data
- Golden queries (known correct answers);
- Adversarial queries (trying to break it);
- Edge cases (empty results, 10K results).
Generation testing
- Faithfulness: Does output match retrieved context?
- Relevance: Does it answer the question?
- Safety: No PII, no hallucination, no harmful content.
Metrics
- BLEU/ROUGE scores (automated);
- Human evaluation (sample-based);
- Citation accuracy (100% required).
Component testing establishes your baseline. If retrieval fails or generation hallucinates at this level, don’t proceed to integration. Fix the fundamentals first.
Level 2: Integration Testing
Here we test the complete RAG pipeline as users would experience it. You should no longer test the components — test real business scenarios instead.
End-to-End user journeys. We create realistic test scenarios based on actual user needs. For example: “A customer service agent needs to handle a refund request for a closed account.”
Cross-source integration. Real queries often require information from multiple sources. We test whether the system correctly combines information from different documents, resolves conflicts between old and new policies, maintains consistency, and properly weights authoritative sources over secondary ones.
For instance, if the 2023 manual says one thing, but the 2025 update says another, does the system recognize and use the latest version?
Performance under load. Simulate production conditions with concurrent users, varying query complexity, and realistic data volumes. Measure whether response quality degrades under load, if retrieval accuracy drops when the system is busy, and whether the system maintains SLAs during peak usage.
This includes testing graceful degradation — if one component slows down, does the system adapt, or does it cascade into total failure?
Level 3: Chaos Testing
This is where you intentionally break things to understand failure modes. You need to know how the system behaves, not when everything works, but when everything goes wrong.
Failure injection scenarios. Systematically introduce failures to see how the system responds.
- What happens when the vector database returns no results?
- When it returns 10,000 documents instead of 10?
- When the embedding service times out mid-query?
- When the LLM API is rate-limited?
Verify that the system fails gracefully, provides meaningful error messages to users, logs sufficient information for debugging, and has working fallback mechanisms.
Adversarial testing. Test with inputs designed to confuse or break the system. This includes contradictory queries, recursive references, malformed data that could corrupt the retrieval process, and attempts to extract training data or bypass safety measures.
Data corruption and drift. Simulate real-world data problems: documents that have been incorrectly indexed, embeddings that have degraded over time, new terminology that doesn’t match old documents, and knowledge bases with conflicting information.
Recovery testing. After each failure scenario, we test whether the system recovers properly.
- Does it clear corrupted caches?
- Resume normal operation without manual intervention?
- Maintain data consistency after partial failures?
- Generate accurate audit logs of what went wrong?
This ensures that temporary problems don’t become permanent degradations.
Moving Forward with RAG Testing
RAG systems are becoming the standard for enterprise AI — not because they’re perfect, but because they’re auditable. They transform the black box of LLMs into a traceable pipeline, where every answer has a receipt.
Start small, but start now:
- Pick one use case (regulatory test generation, defect triage, or synthetic data);
- Index your best 1,000 test cases and requirements;
- Run Level 1 component tests to establish baselines;
- Measure improvement in test coverage or design time;
- Scale based on proven value.
For QA teams, this is both a challenge and an opportunity. The challenge: you’re testing distributed systems with ML components, not just prompts and responses. The opportunity: enterprises desperately need QA expertise that understands both traditional testing and AI validation.
Igor Kovalenko is Engineering Quality Leader at TestFort, software testing company. He partners with enterprise teams on QA audits, creates testing roadmaps, and cloud-ready frameworks that cut release cycles.
Igor brings 9+ years of FinTech experience and a background in banking operations. He is ISTQB Advanced Level Test Manager certified and shares practical insights in his “QA Ground and Grind” newsletter.
