Generative AI Diagnostic Review
What is Gen AI Diagnostic Review?
A Gen AI Diagnostic Review is the process of evaluating an already deployed AI system to understand how well it performs in real-world use. This involves testing the model with diverse queries to see how accurately it responds, identifying hallucinations, and checking whether the RAG pipeline is complete or missing key data. The review also assesses prompt-handling, safety, and alignment to ensure the AI behaves as intended. It’s an efficient way to uncover gaps, improve reliability, and enhance overall performance without rebuilding your system from scratch.
Problem Identification
We begin by identifying the exact goals and expected behaviors of your deployed AI system. Once your use case and evaluation criteria are defined, we outline the test scenarios, failure modes, and performance indicators needed for a diagnostic review.
Data Structure & Evaluation
We examine how your AI uses internal and external knowledge sources, reviewing RAG pipelines, document coverage, and data structuring. This includes checking embeddings, chunking strategies, retrieval accuracy, and content freshness.
Behavioral Testing & Stress Analysis
We stress-test your application under real and adversarial conditions, evaluating prompt handling, hallucination rates, and reasoning quality. Our team tests for weaknesses such as missing retrievals, broken chains of thought, and vulnerability to prompt injection.
Validation & Improvement
We validate your AI’s performance using targeted benchmarks, scenario-based testing, and accuracy measurements. Once results are analyzed, we deliver a structured improvement plan detailing fixes for RAG gaps, tuning needs, etc.