When customers ask, “Is your QA process up to our standards?” — our answer is always the same: we build our QA process around your requirements. At DATAmundi, we partner with you to ensure the datasets we deliver meet your exact expectations and fuel your AI model with reliable, high-quality training data.
Our goal is simple: to provide you with datasets that act as a single source of truth for your AI systems. Here’s how we ensure quality across transcription, annotation, supervised fine-tuning (SFT), and retrieval-augmented generation (RAG).
Transcription QA: Ensuring Accuracy and Fidelity
When handling transcription, our focus is on accuracy, completeness, and fidelity to the source audio. Our process typically includes:
- Automated Pre-Checks
Using speech-to-text models, we flag likely errors, out-of-vocabulary terms, or timing mismatches for deeper inspection. - Human Review
Native or fluent speakers manually verify the transcript, correcting any issues with context-aware judgment. - Dual-Pass Review (When Requested)
A second reviewer validates the corrections to enhance consistency and reduce subjectivity. - Guideline Adherence
We follow client-provided style guides to ensure consistency in spelling, punctuation, filler words, and special instructions.
Annotation QA: Context-Driven and Consistent
In annotation tasks, quality means ensuring correctness, consistency, and contextual relevance. We achieve this through:
- Clear Task Instructions
Annotators receive comprehensive guidelines with examples to reduce ambiguity and improve annotation accuracy. - Gold Standard Comparisons
We benchmark outputs against pre-labeled datasets to assess accuracy. - Consensus Methods
Multiple annotations are compared to identify and resolve disagreement or edge cases. - Spot Checks
A sample of annotations is manually reviewed for real-time quality monitoring. - Feedback Loops
Incorrect labels trigger updates to instructions, retraining, and tool refinements to prevent recurrence.
Supervised Fine-Tuning QA: Instruction Following and Alignment
When we fine-tune datasets for model training, our QA checks focus on instruction-following ability, coherence, and alignment with task objectives:
- Prompt-Response Pair Review
We examine if the model’s responses fulfill the prompt and align with ground truth (when available). - Human-in-the-Loop Evaluation
Expert raters assess helpfulness, harmlessness, and honesty — crucial for avoiding bias and toxicity. - Rubric-Based Scoring
Responses are scored across dimensions like factuality, completeness, tone, and clarity. - Iterative Refinement
Poor outputs are used to generate improved training samples, strengthening future responses.
Retrieval-Augmented Generation (RAG) QA: Groundedness and Relevance
For RAG systems, we QA both the retrieval pipeline and the generated outputs to ensure grounding and factual consistency:
- Retrieval QA
- Relevance Validation
We confirm that retrieved documents/snippets are topically relevant and high quality. - Recall and Precision Metrics
On known datasets, we use standard metrics to quantify retrieval effectiveness.
- Generation QA
- Citation Integrity
We check that generated answers correctly cite and rely on retrieved content. - Hallucination Checks
We flag unsupported claims and ensure factual grounding.
- Fact-Checking & Reviews
- Human and Tool-Assisted Verification
Experts backed by digital tools verify claims against source texts. - Groundedness Scoring
We apply custom metrics to rate how well responses reflect source data. - Layered Reviews
High-sensitivity content undergoes two or even three review passes to ensure correctness and safety.
QA Is Front and Center
For generative AI to behave as you envision — without hallucinations or harmful biases — rigorous QA isn’t optional, it’s essential. By applying tailored quality checks to each stage of dataset creation, we help ensure your AI performs accurately, safely, and reliably in production.
Want to design a custom QA plan for your next AI project? Let’s talk.
