RAG Reliability Diagnostic
Find wrong AI answers before customers do.
We stress-test your RAG assistant with messy customer questions, missing facts, edge cases, and unsafe prompts so you know where it can fail before it reaches production.
What we try to break.
Unsupported promisesPrices, warranty, policy, legal, medical, or operational claims without enough evidence.
Retrieval gapsThe assistant misses the right document, product, policy, or exception.
Prompt attacks and confusionCustomers try strange, hostile, incomplete, or contradictory messages.
What you get back.
Failure taxonomyA clear list of answer failure types and where they come from.
Safer answer rulesWhen to answer, ask a question, refuse, cite a source, or hand off to a human.
Retest-ready casesA test set you can run again after changes to prove the assistant improved.
What is tested in a RAG reliability diagnostic? For technical buyers who want to understand the evaluation scope before sending logs, prompts, or knowledge samples. View technical details
Retrieval quality
We test whether the assistant retrieves the correct source documents, chunks, products, policies, and exceptions before it generates an answer.
RAG evaluation
retrieval testing
vector search audit
source grounding
Answer behavior
We test whether the assistant answers only when it has enough evidence, asks for missing details, refuses unsafe requests, and avoids confident invention.
hallucination testing
AI answer validation
LLM guardrails
AI QA
Why a normal chatbot test is not enough A few happy-path questions do not reveal what happens with incomplete, hostile, vague, or contradictory customer messages. View technical details
Adversarial scenarios
- Questions with missing model, SKU, service type, date, or warranty condition.
- Requests that pressure the assistant into a guarantee or discount.
- Prompt-injection attempts and role confusion.
- Multi-turn conversations where context drifts over time.
Business risk checks
- Unsupported price or warranty promises.
- Wrong product/service recommendations.
- Unsafe advice where a human should decide.
- Private source or internal instruction leakage.
What data is needed for a first diagnostic? The first pass can be done without live database access or production admin credentials. View technical details
Low-access inputs
- 20-100 anonymized customer questions or chat logs.
- Representative FAQ, product, service, policy, or warranty documents.
- Current system prompt or answer policy if available.
- Examples of answers that felt wrong, risky, or incomplete.
Diagnostic output
- Failure map by severity and frequency.
- Recommended refusal, clarification, and handoff rules.
- Retest cases for future regression checks.
- Optional implementation plan for safer RAG behavior.
Reliability Engineering
Find incorrect AI answers before your customers do.
OpsBalance performs adversarial stress testing, retrieval auditing, and hallucination checks on your RAG assistants — exposing failure surfaces before production.
Hallucinated Policy
Confidence erring on unsupported warranties or price promises.
Retrieval Gaps
Missing critical document chunks during semantic search query cycles.
Context Drift
Losing system parameters or instruction boundaries over multi-turn chats.
Stale Knowledge
Querying outdated product documents or obsolete catalog caches.
Prompt Injection
System leaks caused by malicious customer formatting commands.
Diagnostic Matrix
Our adversarial evaluation taxonomy.
We test RAG models with 50+ localized failure scenarios, evaluating the system's ability to refuse rather than invent parameters.
| Vulnerability Class | Technical Cause | Business Risk | Remediation Action |
|---|---|---|---|
| Unsupported Product Claims | Cos. similarity thresholds too low, prompting generative guesses. | High Liability | Enforce `Refuse > Invent` parameter rules with strict semantic gates. |
| Source Citation Leaks | Unstructured vector ingestion exposing raw text metadata chunks. | Data Privacy Breach | Implement citation sanitizers and PII masks inside context nodes. |
| System Instruction Override | Weak system prompt anchoring; easily bypassed by jailbreak inputs. | Brand Reputation Damage | Integrate an independent Adjudicator node to audit generated text before dispatch. |
| Unvalidated API Payload | Lacking schema constraints in generative tool-calling parameters. | Workflow Failure | Apply strict type validations (Pydantic schemas) on all external payloads. |
Audit Methodology
Our systematic diagnostic workflow.
We do not disrupt your production services. All evaluations are carried out in a clean local test suite, using simulated client traffic.
01
Collect Logs
We ingest 100-200 raw, anonymized customer inquiries and your active grounding documents.
02
Synthesize Evals
We build a custom adversarial evaluation prompt set tailored to your exact business parameters.
03
Execute Triage
We run isolated multi-agent checks (LangGraph + Qdrant) to detect logical drift and factual errors.
04
Remediate
Receive a clear failure taxonomy report, Pydantic type checkers, and system-level guardrail models.
Trust Boundary
No production database access required.
We respect customer privacy and secure corporate assets. Initial diagnostic stress tests are executed entirely from redacted JSON logs and static catalogs.
- No live database connections or system administrative credentials requested.
- FOP (Fractional Operator) oversight: all rules and remediations undergo master review.
- GDPR-aware configuration data handling for all European and German B2B clients.
Secure your assistant before launch.
Send us your active RAG system parameters or 20 typical conversation logs. We will return a preliminary failure surface map and a fixed-price diagnostic proposal.