LLM Testing
Fireworks AI differentiates itself with developer experience: fastest cold start times in the serverless LLM market, a compound AI system builder (FireFunction), and transparent pricing. Testing Fireworks AI integrations means validating not just completion quality but the specific behaviors of their serverless infrastructure and FireFunction orchestration layer. This guide covers
LLM Testing
Fine-tuning a language model improves it on your specific task — but at a cost. Fine-tuned models can regress on capabilities the base model had, forget general knowledge, or overfit to training data in ways that hurt generalization. This guide covers how to evaluate fine-tuned models systematically: benchmark
RAG
RAG (Retrieval-Augmented Generation) systems fail in ways that are invisible without the right tests. The LLM might generate confident-sounding output that's completely ungrounded in the retrieved documents. The retriever might fetch irrelevant documents. The response might be faithful to the retrieved context but that context might
LLM Testing
Changing a prompt is a code change. It can break features silently — the LLM still returns output, but the quality drops. Without a regression suite, you discover prompt regressions in production through user complaints, not tests. This guide covers how to build CI-integrated prompt regression testing that catches quality
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.
LLM Testing
Semantic Kernel (SK) applications combine LLM calls, plugin functions, memory retrieval, and multi-step planning into a single execution graph.
LLM Testing
Retrieval-Augmented Generation (RAG) pipelines are notoriously hard to evaluate.
LLM Testing
Most LLM security testing is manual: a human tries to jailbreak the model, documents what worked, and moves on.
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
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
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