LLM application testing services

Challenge

The same prompt can return different results due to temperature, context length, or model updates.

Solution

Semantic assertions, tolerance thresholds, and statistical baselines that validate intent and quality, not exact text.

Challenge

Most LLM use cases don’t have a single right answer. Teams struggle to turn subjective expectations into testable criteria.

Solution

Rubric-based evaluation models with measurable quality gates (accuracy, relevance, safety, grounding), combining automation with targeted human review.

Challenge

Over 70% of LLM failures happen outside the model, in prompts, retrieval logic, chunking, or tool orchestration.

Solution

End-to-end testing of the full pipeline: prompt templates, retrieval quality, tool calls, retries, and fallback logic.

Challenge

Model or embedding updates can change behavior even when your code doesn’t, causing unnoticed production regressions.

Solution

Versioned semantic baselines, regression suites, and canary evaluations to detect behavioral drift early.

Challenge

Hallucinations remain one of the top reasons users lose trust in GenAI products, especially in regulated domains.

Solution

Grounding checks, contradiction detection, RAG validation, and confidence scoring to reduce false or invented outputs.

Challenge

Prompt injection, indirect data leakage, and unsafe tool execution are now common LLM-specific attack vectors.

Solution

Adversarial testing, prompt hardening, access-boundary validation, and abuse-case simulations built into QA.