6 posts about LLM testing by 4 contributors.
Know LLM testing? Share what you know with developers who are learning it.
Learn how to evaluate a Python RAG pipeline with RAGAS using faithfulness, context precision, context recall, and answer relevancy metrics.
Test AI agents with synthetic enterprise documents to evaluate extraction accuracy, grounding, hallucinations, edge cases, document variations, and system reliability under controlled conditions.
Build automated evaluation gates for AI agents to detect tool-calling regressions when prompts, schemas, models, or agent workflows change across development and deployment.
Master AI quality assurance for LLM apps. Explore testing strategies for accuracy, relevance, security, and reliability.
Promptfoo is an open-source tool revolutionizing LLM prompt engineering. It enables systematic testing, evaluation, and benchmarking of prompts, ensuring reliable, unbiased, and cost-effective results. Automate regression testing, compare models (GPT-4, Llama 3), and integrate human evaluation for subjective outputs.
Evaluating prompt effectiveness is key to successful prompt engineering. Learn the metrics, methods, and tools to measure how well prompts guide AI models in producing accurate and reliable results.
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