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Promptbase is a prompt engineering framework designed for designing, testing, and optimizing prompts for large language models. It provides a system for measuring model accuracy and performance through an evaluation toolkit that compares outputs against ground-truth datasets. The project also includes an orchestration pipeline for automating multi-component machine learning tasks across cloud-based endpoints and a utility for preparing retrieval-augmented generation datasets.
The main features of microsoft/promptbase are: Prompt Engineering Frameworks, Prompt Evaluation Tools, AI Model Benchmarking, Automated Chain-of-Thought, Chain-of-Thought Prompting, Few-Shot Optimizers, Answer Accuracy Evaluators, LLM Workflow Orchestrations.
Projects with overlapping indexed features include: vibrantlabsai/ragas — Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and… typpo/promptfoo — promptfoo is an evaluation framework for measuring the performance of large language model prompts, agents, and… agenta-ai/agenta — Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from… mrdbourke/zero-to-mastery-ml — This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter… mshumer/gpt-prompt-engineer — This project is an automated prompt engineering and optimization tool designed to iteratively create, test, and refine… arize-ai/phoenix — Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and…
Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin
promptfoo is an evaluation framework for measuring the performance of large language model prompts, agents, and retrieval augmented generation pipelines. It provides a suite of tools for conducting comparative benchmarking and executing automated quality and security regressions. The system features a benchmarking suite for running identical prompts across different model providers to compare output quality side-by-side. It also includes a dedicated red teaming tool for identifying security vulnerabilities and prompt injection risks through automated penetration testing. The framework suppor
Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove
This project is a machine learning educational curriculum and learning platform delivered through interactive Jupyter Notebooks. It serves as a comprehensive guide for mastering the Python data science toolkit, providing structured tutorials for numerical computing, tabular data manipulation, and statistical visualization. The curriculum includes specific implementation guides for Scikit-Learn and a practical course on TensorFlow for constructing, training, and deploying neural networks and computer vision models. It covers the end-to-end process of building predictive models, from initial pr