AI Testing
LLMs encode the biases present in their training data — and those biases surface in completions in ways that are measurable and testable. This guide covers what LLM bias looks like in practice, how to measure it with established benchmarks and DeepEval, how to build a bias test suite, and how
LLM Testing
Vertex AI provides a dedicated Evaluation Service for scoring generative model outputs — no third-party judge needed. Combined with Model Monitoring for skew and drift detection, you can build a complete quality assurance loop that runs on every deployment. Vertex AI Evaluation Service Overview The Evaluation Service scores model responses using:
LLM Testing
Semantic Kernel (SK) applications combine LLM calls, plugin functions, memory retrieval, and multi-step planning into a single execution graph. Testing each layer in isolation — without making real API calls — is the difference between a test suite that runs in 2 seconds and one that costs $50 per CI run. Project
LLM Testing
Retrieval-Augmented Generation (RAG) pipelines are notoriously hard to evaluate. A model can produce fluent, confident-sounding answers that are completely unsupported by the retrieved documents. RAGAS gives you a structured framework to measure exactly that — and to wire it into your CI pipeline so regressions surface before they reach production. What
LLM Testing
Most LLM security testing is manual: a human tries to jailbreak the model, documents what worked, and moves on. That approach doesn't scale and doesn't catch regressions when you swap models or update system prompts. Garak is an open-source LLM vulnerability scanner that automates this process
LLM Testing
AWS Bedrock Model Evaluation lets you run structured quality assessments against Bedrock-hosted models — comparing outputs against ground truth, scoring on custom criteria, and tracking results across model versions. Here's how to set up automated evaluation pipelines using the Bedrock API and boto3. What Bedrock Model Evaluation Offers Bedrock
AI Testing
Real test data is scarce, sensitive, and hard to maintain. LLMs can generate realistic synthetic test data at scale — covering edge cases that real datasets miss and eliminating PII concerns. This guide covers practical techniques for generating test data with LLMs: schema-based generation, edge case discovery, data augmentation, and validating
AI Testing
Prompts are code. Changing a single word in a system prompt can break a feature that was working fine. Without version control and regression testing for prompts, you're flying blind — every prompt change is a gamble. This guide covers how to version control prompts, run regression evals on
AI Testing
Eval-driven development (EDD) applies the discipline of TDD to LLM application development. Instead of writing unit tests for deterministic code, you write evals that define what "good" looks like for your AI — before building the feature. Failing evals are your spec. This guide covers how to structure evals
AI Testing
Evaluating a traditional machine learning model is a solved problem. You have a test set, a metric, and a number. Evaluating an agentic AI pipeline is not solved, and the gap between the two is larger than most teams expect when they first try to apply standard ML evaluation thinking
AI Testing
A golden dataset is a curated set of inputs with known-good outputs (or evaluation criteria) used to measure whether your LLM application is working correctly. Without one, you're evaluating against vibes. This guide covers how to build, annotate, version, and maintain golden datasets for LLM applications — from initial
AI Testing
Testing LLM applications is hard because the outputs are open-ended text — there's no single correct answer. LLM-as-judge sidesteps this by using a capable model to score another model's outputs according to defined rubrics. This guide covers the key evaluation patterns: G-Eval for rubric-based scoring, pairwise comparison