Best AI tools for< Evaluation >

User Evaluation is an AI-powered insights and analysis tool that offers a comprehensive platform for customer understanding. It provides advanced features such as AI-generated reports and presentations, sentiment analysis, transcription solutions, multimodal AI chat, and diverse data sources analysis. The tool helps businesses streamline data discovery, convert customer data into strategic assets, and uncover actionable customer insights with the power of AI.

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.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Creators is a website that offers a service to create pitch decks for startups and growing businesses. They specialize in creating visually stunning and impactful pitch decks that tell the story of the business and capture the attention of investors. They use a data-driven approach to storytelling, incorporating relevant data and analytics to back up the idea and prove its potential to investors. They also use artificial intelligence to identify the most compelling way to present the information, ensuring that the pitch deck is not just informative, but also engaging. Creators has a team of expert designers who excel at transforming complex ideas into clear, understandable visuals that are both stunning and highly effective in communicating the message to potential investors.

Resumecheck.net is an AI-powered resume improvement platform that helps users create error-free, professional resumes that stand out to recruiters. The platform uses GPT4 technology to provide personalized feedback and suggestions, including grammar corrections, formatting adjustments, and industry-specific keyword optimization. Additionally, Resumecheck.net offers an AI Cover Letter Writer that generates tailored cover letters based on the user's resume and the specific job position they are applying for.

Loupe Recruit is an AI-powered talent assessment platform that helps recruiters and hiring managers assess job descriptions and talent faster and more efficiently. It uses natural language processing and machine learning to analyze job descriptions and identify the key skills and experience required for a role. Loupe Recruit then matches candidates to these requirements, providing recruiters with a ranked list of the most qualified candidates. The platform also includes a variety of tools to help recruiters screen and interview candidates, including video interviewing, skills assessments, and reference checks.

VMock Dashboard is an AI-powered platform designed to provide personalized feedback on resumes. It leverages machine learning algorithms to analyze resumes and offers detailed insights on areas of improvement. Users can upload their resumes, receive instant feedback on content, formatting, and overall effectiveness, and track their progress over time. VMock Dashboard aims to help individuals enhance their resumes and increase their chances of landing their desired job opportunities.

Career Copilot is an AI-powered hiring tool that helps recruiters and hiring managers find the best candidates for their open positions. The tool uses machine learning to analyze candidate profiles and identify those who are most qualified for the job. Career Copilot also provides a number of features to help recruiters streamline the hiring process, such as candidate screening, interview scheduling, and offer management.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

RebeccAi is an AI-powered business idea evaluation and validation tool that uses AI technology to provide accurate insights into the potential of users' ideas. It helps users refine and improve their ideas quickly and intelligently, serving as a one-person team for business dreamers. The platform assists in turning ideas into reality, from business concepts to creative projects, by leveraging the latest AI tools and technologies to innovate faster and smarter.

Hamming is an AI tool designed to help automate voice agent testing and optimization. It offers features such as prompt optimization, automated voice testing, monitoring, and more. The platform allows users to test AI voice agents against simulated users, create optimized prompts, actively monitor AI app usage, and simulate customer calls to identify system gaps. Hamming is trusted by AI-forward enterprises and is built for inbound and outbound agents, including AI appointment scheduling, AI drive-through, AI customer support, AI phone follow-ups, AI personal assistant, and AI coaching and tutoring.

Airtrain is a no-code compute platform for Large Language Models (LLMs). It provides a user-friendly interface for fine-tuning, evaluating, and deploying custom AI models. Airtrain also offers a marketplace of pre-trained models that can be used for a variety of tasks, such as text generation, translation, and question answering.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

Checkmyidea-IA is an AI-powered tool that helps entrepreneurs and businesses evaluate their business ideas before launching them. It uses a variety of factors, such as customer interest, uniqueness, initial product development, and launch strategy, to provide users with a comprehensive review of their idea's potential for success. Checkmyidea-IA can help users save time, increase their chances of success, reduce risk, and improve their decision-making.

JobXRecruiter is an AI-powered CV review tool designed for recruiters to streamline the candidate evaluation process. It automates the review of resumes, provides detailed candidate analysis, and helps recruiters save time by focusing on hiring rather than manual screening. The tool offers a 1-minute setup, reduces candidate evaluation time, and eliminates tedious screening tasks. With JobXRecruiter, recruiters can create projects for each vacancy, receive match scores for candidates, and easily shortlist the best candidates without opening individual CVs. The application is secure, efficient, and a game-changer for recruiters looking to optimize their hiring process.

Wisedocs is an AI-powered platform that specializes in medical record reviews, summaries, and insights for claims processing. The platform offers intelligent features such as medical chronologies, workflows, deduplication, intelligent OCR, and insights summaries. Wisedocs streamlines the process of reviewing medical records for insurance, legal, and independent medical evaluation firms, providing speed, accuracy, and efficiency in claims processing. The platform automates tasks that were previously laborious and error-prone, making it a valuable tool for industries dealing with complex medical records.

MTestHub is an all-in-one recruiting and assessment automation solution that streamlines the hiring process for organizations. It harnesses AI technology to identify top talent, provides an authentic online examination platform, and offers features such as auto screening, shortlisting, efficient interviews, and post-shortlisting actions. The platform aims to enhance recruitment efficiency, improve candidate experience, and reduce administrative burdens through automation and data-driven insights.

DisplayGateGuard is a brand safety and suitability provider that leverages AI-powered analysis to help advertisers choose the right placements, isolate fraudulent websites, and enhance brand safety and suitability. The platform offers curated inclusion and exclusion lists to provide deeper insights into the environments and contexts where ads are shown, ensuring campaigns reach the right audience effectively. By utilizing artificial intelligence, DisplayGateGuard assesses websites through diverse metrics to curate placements that align seamlessly with advertisers' specific requirements and values.

CyberRiskAI.com is a website that is currently under development and is registered at Dynadot.com. The website is expected to offer services related to cyber risk management and artificial intelligence in the future. With a focus on cybersecurity and risk assessment, CyberRiskAI.com aims to provide innovative solutions to help businesses mitigate cyber threats and protect their digital assets. The platform is designed to leverage AI technologies to analyze and predict cyber risks, enabling users to make informed decisions to enhance their security posture.

Adminer is a comprehensive platform designed to assist e-commerce entrepreneurs in identifying, analyzing, and validating profitable products. It leverages artificial intelligence to provide users with data-driven insights, enabling them to make informed decisions and optimize their product offerings. Adminer's suite of features includes product research, market analysis, supplier evaluation, and automated copywriting, empowering users to streamline their operations and maximize their sales potential.

The LLM Evaluation guidebook provides comprehensive guidance on evaluating language model performance, including different evaluation methods, designing evaluations, and practical tips. It caters to both beginners and advanced users, offering insights on model inference, tokenization, and troubleshooting. The guide covers automatic benchmarks, human evaluation, LLM-as-a-judge scenarios, troubleshooting practicalities, and general knowledge on LLM basics. It also includes planned articles on automated benchmarks, evaluation importance, task-building considerations, and model comparison challenges. The resource is enriched with recommended links and acknowledgments to contributors and inspirations.

Agent Evaluation is a generative AI-powered framework for testing virtual agents. It implements an LLM agent (evaluator) to orchestrate conversations with your own agent (target) and evaluate responses. It supports popular AWS services, allows concurrent multi-turn conversations, defines hooks for additional tasks, and can be used in CI/CD pipelines for faster delivery and stable production environments.

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron. We're releasing it with the community in the spirit of building in the open. Note that it is still very much early so don't expect 100% stability ^^' In case of problems or question, feel free to open an issue!

TurtleBench is a dynamic evaluation benchmark that assesses the reasoning capabilities of large language models through real-world yes/no puzzles. It emphasizes logical reasoning over knowledge recall by using user-generated data from a Turtle Soup puzzle platform. The benchmark is objective and unbiased, focusing purely on reasoning abilities and providing clear, measurable outcomes for easy comparison. TurtleBench constantly evolves with real user-generated questions, making it impossible to 'game' the system. It tests the model's ability to comprehend context and make logical inferences.

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.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

Awesome-LLM-Eval: a curated list of tools, benchmarks, demos, papers for Large Language Models (like ChatGPT, LLaMA, GLM, Baichuan, etc) Evaluation on Language capabilities, Knowledge, Reasoning, Fairness and Safety.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.

MathEval is a benchmark designed for evaluating the mathematical capabilities of large models. It includes over 20 evaluation datasets covering various mathematical domains with more than 30,000 math problems. The goal is to assess the performance of large models across different difficulty levels and mathematical subfields. MathEval serves as a reliable reference for comparing mathematical abilities among large models and offers guidance on enhancing their mathematical capabilities in the future.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

EvalPlus is a rigorous evaluation framework for LLM4Code, providing HumanEval+ and MBPP+ tests to evaluate large language models on code generation tasks. It offers precise evaluation and ranking, coding rigorousness analysis, and pre-generated code samples. Users can use EvalPlus to generate code solutions, post-process code, and evaluate code quality. The tool includes tools for code generation and test input generation using various backends.

Inspect AI is a framework developed by the UK AI Safety Institute for evaluating large language models. It offers various built-in components for prompt engineering, tool usage, multi-turn dialog, and model graded evaluations. Users can extend Inspect by adding new elicitation and scoring techniques through additional Python packages. The tool aims to provide a comprehensive solution for assessing the performance and safety of language models.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Simply write any topic (anything superheroes, vacuums, Pokémon’, diamonds…) and I’ll provide the evaluation criteria you can use.

Interactive system for detailed self-evaluations in PDF format.

FactCheck Navigator GPT is designed for in-depth fact checking and analysis of written content and evaluation of its source. The approach is to iterate through predefined and well-prompted steps. If desired, the user can refine the process by providing input between these steps.

Assesses and recommends potential suppliers for organizational needs.

Legal assistant specializing in complex visa cases.

Optimizes project outcomes through comprehensive post-project evaluations.

A professional assistant for technical item evaluations in banking

Analyzes CME evaluation data for insights.

Helps with valuation and evaluation of your patent

A career coach aiding in writing self-evaluations.

Assists in vendor risk evaluation for security, legal, and privacy.

Multiple Environment Threat Evaluation of Resources (METEOR)™ Space Threats and Operational Risks to Mission (STORM)™ non-profit product AI co-pilot

Assists with web accessibility evaluations

Startup advisor guiding founders through detailed idea evaluation, product-market-fit, business model, GTM, and scaling.

Expert in craft beer stories and evaluations.

VC startup analyst for in-depth startup evaluations

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

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

Ensures product quality through systematic process monitoring and evaluation.

Direct, concise value prop evaluations with scoring.