Best AI tools for< Evaluate System Effectiveness >

Mangus is an AI-powered learning platform that provides personalized learning paths for employees and students. It offers a wide range of courses and programs in various disciplines, including business, education, technology, and more. Mangus uses gamification and artificial intelligence to create an engaging and effective learning experience.

PyjamaHR is a leading AI-powered Applicant Tracking System (ATS) and recruitment software designed to streamline the hiring process for businesses of all sizes. It offers advanced features such as source management, candidate evaluation, collaboration tools, and AI-powered candidate tests to enhance the efficiency and effectiveness of the recruitment process. With a user-friendly interface and robust security measures, PyjamaHR is a trusted solution for managing talent acquisition and improving hiring outcomes.

Workable is a leading recruiting software and hiring platform that offers a full Applicant Tracking System with built-in AI sourcing. It provides a configurable HRIS platform to securely manage employees, automate hiring tasks, and offer actionable insights and reporting. Workable helps companies streamline their recruitment process, from sourcing to employee onboarding and management, with features like sourcing and attracting candidates, evaluating and collaborating with hiring teams, automating hiring tasks, onboarding and managing employees, and tracking HR processes.

Flow AI is an advanced AI tool designed for evaluating and improving Large Language Model (LLM) applications. It offers a unique system for creating custom evaluators, deploying them with an API, and developing specialized LMs tailored to specific use cases. The tool aims to revolutionize AI evaluation and model development by providing transparent, cost-effective, and controllable solutions for AI teams across various domains.

Recooty is a modern applicant tracking system designed for growing companies to streamline their recruiting process. It offers features such as applicant tracking, job posting, candidate tracking, interview scheduling, talent pool management, employer branding, and HR tools. With Recooty, companies can attract, engage, and hire their next teammates with ease. The platform also provides resources like job descriptions, templates, interview questions, and AI tools to enhance the recruitment experience.

PolygrAI is a digital polygraph powered by AI technology that provides real-time risk assessment and sentiment analysis. The platform meticulously analyzes facial micro-expressions, body language, vocal attributes, and linguistic cues to detect behavioral fluctuations and signs of deception. By combining well-established psychology practices with advanced AI and computer vision detection, PolygrAI offers users actionable insights for decision-making processes across various applications.

Wizi AI is a technical AI interviewer that helps employers evaluate hundreds of candidates with in-depth assessments. It goes beyond basic coding challenges and conducts an onsite interview experience for every candidate. Employers get actionable hiring signals with in-depth reports on system design, project implementation, domain expertise, and debugging skills. Wizi AI saves teams time by screening all candidates with AI and bringing only the best to onsites.

Sacred is a tool to configure, organize, log and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments. The ability to conveniently make experiments configurable is at the heart of Sacred. If the parameters of an experiment are exposed in this way, it will help you to: keep track of all the parameters of your experiment easily run your experiment for different settings save configurations for individual runs in files or a database reproduce your results In Sacred we achieve this through the following main mechanisms: Config Scopes are functions with a @ex.config decorator, that turn all local variables into configuration entries. This helps to set up your configuration really easily. Those entries can then be used in captured functions via dependency injection. That way the system takes care of passing parameters around for you, which makes using your config values really easy. The command-line interface can be used to change the parameters, which makes it really easy to run your experiment with modified parameters. Observers log every information about your experiment and the configuration you used, and saves them for example to a Database. This helps to keep track of all your experiments. Automatic seeding helps controlling the randomness in your experiments, such that they stay reproducible.

NexLev is an AI-powered YouTube niche finder that helps users discover untapped, high-demand niches in seconds. It provides detailed YouTube channel analytics and an intuitive filtering system, making it easier for users to find lucrative niches for their next YouTube venture. NexLev also offers a variety of features such as a keyword analysis tool, competitor analysis, and a niche profitability calculator.

SiMa.ai is an AI application that offers high-performance, power-efficient, and scalable edge machine learning solutions for various industries such as automotive, industrial, healthcare, drones, and government sectors. The platform provides MLSoC™ boards, DevKit 2.0, Palette Software 1.2, and Edgematic™ for developers to accelerate complete applications and deploy AI-enabled solutions. SiMa.ai's Machine Learning System on Chip (MLSoC) enables full-pipeline implementations of real-world ML solutions, making it a trusted platform for edge AI development.

CloudExam AI is an online testing platform developed by Hanke Numerical Union Technology Co., Ltd. It provides stable and efficient AI online testing services, including intelligent grouping, intelligent monitoring, and intelligent evaluation. The platform ensures test fairness by implementing automatic monitoring level regulations and three random strategies. It prioritizes information security by combining software and hardware to secure data and identity. With global cloud deployment and flexible architecture, it supports hundreds of thousands of concurrent users. CloudExam AI offers features like queue interviews, interactive pen testing, data-driven cockpit, AI grouping, AI monitoring, AI evaluation, random question generation, dual-seat testing, facial recognition, real-time recording, abnormal behavior detection, test pledge book, student information verification, photo uploading for answers, inspection system, device detection, scoring template, ranking of results, SMS/email reminders, screen sharing, student fees, and collaboration with selected schools.

Applicant AI is an applicant tracking and recruiting software powered by AI (ATS). It helps companies streamline their hiring process by using AI to screen and evaluate job applicants, saving time and effort. The tool offers features like pre-screening applicants, scoring candidates, and integrating with existing ATS platforms. Applicant AI is designed to prioritize high-quality applicants and improve the efficiency of the recruitment process.

Fairo is a platform that facilitates Responsible AI Governance, offering tools for reducing AI hallucinations, managing AI agents and assets, evaluating AI systems, and ensuring compliance with various regulations. It provides a comprehensive solution for organizations to align their AI systems ethically and strategically, automate governance processes, and mitigate risks. Fairo aims to make responsible AI transformation accessible to organizations of all sizes, enabling them to build technology that is profitable, ethical, and transformative.

Stanford HAI is a research institute at Stanford University dedicated to advancing AI research, education, and policy to improve the human condition. The institute brings together researchers from a variety of disciplines to work on a wide range of AI-related projects, including developing new AI algorithms, studying the ethical and societal implications of AI, and creating educational programs to train the next generation of AI leaders. Stanford HAI is committed to developing human-centered AI technologies and applications that benefit all of humanity.

Maxim is an end-to-end AI evaluation and observability platform that empowers modern AI teams to ship products with quality, reliability, and speed. It offers a comprehensive suite of tools for experimentation, evaluation, observability, and data management. Maxim aims to bring the best practices of traditional software development into non-deterministic AI workflows, enabling rapid iteration and deployment of AI models. The platform caters to the needs of AI developers, data scientists, and machine learning engineers by providing a unified framework for evaluation, visual flows for workflow testing, and observability features for monitoring and optimizing AI systems in real-time.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Compassionate AI is a cutting-edge AI-powered platform that empowers individuals and organizations to create and deploy AI solutions that are ethical, responsible, and aligned with human values. With Compassionate AI, users can access a comprehensive suite of tools and resources to design, develop, and implement AI systems that prioritize fairness, transparency, and accountability.

AutoScreen is an AI-powered recruitment tool that revolutionizes the hiring process. It utilizes advanced algorithms and machine learning to streamline the recruitment process, saving time and resources for businesses. With AutoScreen, employers can efficiently screen and shortlist candidates based on predefined criteria, leading to faster and more accurate hiring decisions. The tool offers a user-friendly interface, customizable features, and seamless integration with existing HR systems, making it a valuable asset for modern recruitment practices.

LlamaIndex is a framework for building context-augmented Large Language Model (LLM) applications. It provides tools to ingest and process data, implement complex query workflows, and build applications like question-answering chatbots, document understanding systems, and autonomous agents. LlamaIndex enables context augmentation by combining LLMs with private or domain-specific data, offering tools for data connectors, data indexes, engines for natural language access, chat engines, agents, and observability/evaluation integrations. It caters to users of all levels, from beginners to advanced developers, and is available in Python and Typescript.

Pangea.ai is a leading talent aggregator that helps businesses hire quality technologists by comparing data points for reliable matching. It offers a unified hiring experience in a fragmented market, making it easier to compare and decide among the numerous software development agencies and talent networks available. Pangea.ai's intelligent matching system considers over 100 data points to find the best fit for businesses, while its rigorous vetting process evaluates expertise, client satisfaction, and team health. Businesses can choose to self-serve their way to a hire or opt for Pangea.ai's white-glove matching service.

The UltraRAG framework is a researcher and developer-friendly RAG system solution that simplifies the process from data construction to model fine-tuning in domain adaptation. It introduces an automated knowledge adaptation technology system, supporting no-code programming, one-click synthesis and fine-tuning, multidimensional evaluation, and research-friendly exploration work integration. The architecture consists of Frontend, Service, and Backend components, offering flexibility in customization and optimization. Performance evaluation in the legal field shows improved results compared to VanillaRAG, with specific metrics provided. The repository is licensed under Apache-2.0 and encourages citation for support.

LLMInterviewQuestions is a repository containing over 100+ interview questions for Large Language Models (LLM) used by top companies like Google, NVIDIA, Meta, Microsoft, and Fortune 500 companies. The questions cover various topics related to LLMs, including prompt engineering, retrieval augmented generation, chunking, embedding models, internal working of vector databases, advanced search algorithms, language models internal working, supervised fine-tuning of LLM, preference alignment, evaluation of LLM system, hallucination control techniques, deployment of LLM, agent-based system, prompt hacking, and miscellaneous topics. The questions are organized into 15 categories to facilitate learning and preparation.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

RAGElo is a streamlined toolkit for evaluating Retrieval Augmented Generation (RAG)-powered Large Language Models (LLMs) question answering agents using the Elo rating system. It simplifies the process of comparing different outputs from multiple prompt and pipeline variations to a 'gold standard' by allowing a powerful LLM to judge between pairs of answers and questions. RAGElo conducts tournament-style Elo ranking of LLM outputs, providing insights into the effectiveness of different settings.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

MAVIS (Math Visual Intelligent System) is an AI-driven application that allows users to analyze visual data such as images and generate interactive answers based on them. It can perform complex mathematical calculations, solve programming tasks, and create professional graphics. MAVIS supports Python for coding and frameworks like Matplotlib, Plotly, Seaborn, Altair, NumPy, Math, SymPy, and Pandas. It is designed to make projects more efficient and professional.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

Proactive Agent is a project aimed at constructing a fully active agent that can anticipate user's requirements and offer assistance without explicit requests. It includes a data collection and generation pipeline, automatic evaluator, and training agent. The project provides datasets, evaluation scripts, and prompts to finetune LLM for proactive agent. Features include environment sensing, assistance annotation, dynamic data generation, and construction pipeline with a high F1 score on the test set. The project is intended for coding, writing, and daily life scenarios, distributed under Apache License 2.0.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

Advanced RAG Techniques is a comprehensive collection of cutting-edge Retrieval-Augmented Generation (RAG) tutorials aimed at enhancing the accuracy, efficiency, and contextual richness of RAG systems. The repository serves as a hub for state-of-the-art RAG enhancements, comprehensive documentation, practical implementation guidelines, and regular updates with the latest advancements. It covers a wide range of techniques from foundational RAG methods to advanced retrieval methods, iterative and adaptive techniques, evaluation processes, explainability and transparency features, and advanced architectures integrating knowledge graphs and recursive processing.

可以根据所给出的企业合规整改案例，从企业犯罪的内生性结构原因的诊断、制度纠错和专项合规体系构建等三个方面，分析企业合规整改的主要框架，并对合规整改的有效性作出评价。

A System Architect Coach guiding you through system design principles and best practices. Explains CAP theorem like no one else

Advanced epidemiology expert with AI-driven data integration and dynamic visualization tools.

I review system designs and suggest ideal architectures.

Crea pruebas técnicas para programadores.

Expert on Computer Organization and Architecture

Expert in authenticating luxury items, providing detailed evaluations and ratings.

Fun and scholarly insights into systems theory

a daily challenge

Ethics-focused GPT builder assistant based on European AI guidelines, recommendations and regulations

An expert in Drug Delivery Systems Industry, providing in-depth, accurate insights.

Provides expert guidance in transportation engineering projects.

Advises on sustainable engineering solutions to environmental challenges.

Your Edexcel A level maths assistant. Ask for new questions. Help for the next step in your working out. Even send me a picture of a question and i can tell you what exam it is from.

I will score your Mind Blowing Startup Ideas, helping your to evaluate faster.

I'll help you evaluate your writing to make sure it's engaging, informative, and flows well. Uses principles from "Made to Stick"

The main objective of a personalized ChatGPT for reading laboratory tests is to evaluate laboratory test results and create a spreadsheet with the evaluation results and possible solutions.

As a Search Quality Rater, you will help evaluate search engine quality around the world.

Business Model Canvas Creator - Build and evaluate your business model

Guide to unbiasedly evaluate different job opportunities