Best AI tools for< Evaluate Llm Intelligence >

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

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.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

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.

SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.

MailMaestro is an AI email assistant that helps users write better emails faster, allowing them to be more productive and focus on important tasks. The company behind MailMaestro, Maestro Labs, acquired Flowrite, the AI writing division of Flow AI, in September 2024. The team behind Flowrite is now focusing on Flow AI, an advanced platform for AI teams to evaluate and improve LLM systems effortlessly. MailMaestro integrates Flowrite's capabilities to enhance email writing experience for users.

LlamaIndex is a leading data framework designed for building LLM (Large Language Model) applications. It allows enterprises to turn their data into production-ready applications by providing functionalities such as loading data from various sources, indexing data, orchestrating workflows, and evaluating application performance. The platform offers extensive documentation, community-contributed resources, and integration options to support developers in creating innovative LLM applications.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

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.

UpTrain is a full-stack LLMOps platform designed to help users with all their production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and precision metrics to enhance the development of LLM applications. UpTrain is built for developers, by developers, and is compliant with data governance needs. It provides cost efficiency, reliability, and open-source core evaluation framework. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

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.

Fritz AI is an AI tool that scans and ranks all AI tools, apps, and websites based on a set of criteria to determine the best and most ethical options. They provide technical guides, reviews, and tutorials to help users get started with machine learning. Fritz AI focuses on ethics, functionality, user experience, and innovation when evaluating tools. Users can contribute tool suggestions and collaborate with the Fritz AI team. The platform also offers beginner-friendly guides, consulting services, and promotes ethical use of AI and machine learning technologies.

FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.

Teammately is an AI tool that redefines how Human AI-Engineers build AI. It is an Agentic AI for AI development process, designed to enable Human AI-Engineers to focus on more creative and productive missions in AI development. Teammately follows the best practices of Human LLM DevOps and offers features like Development Prompt Engineering, Knowledge Tuning, Evaluation, and Optimization to assist in the AI development process. The tool aims to revolutionize AI engineering by allowing AI AI-Engineers to handle technical tasks, while Human AI-Engineers focus on planning and aligning AI with human preferences and requirements.

Lucida AI is an AI-driven coaching tool designed to enhance employees' English language skills through personalized insights and feedback based on real-life call interactions. The tool offers comprehensive coaching in pronunciation, fluency, grammar, vocabulary, and tracking of language proficiency. It provides advanced speech analysis using proprietary LLM and NLP technologies, ensuring accurate assessments and detailed tracking. With end-to-end encryption for data privacy, Lucy AI is a cost-effective solution for organizations seeking to improve communication skills and streamline language assessment processes.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs). With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment. The Intelligence Layer SDK provides features such as Composability, Evaluability, and Traceability, along with examples to get started. It supports local installation using poetry, integration with Docker, and access to LLM endpoints for tutorials and tasks like Summarization, Question Answering, Classification, Evaluation, and Parameter Optimization. The tool also offers pre-configured tasks for tasks like Classify, QA, Search, and Summarize, serving as a foundation for custom development.

A Rust based tool to evaluate LLM models, prompts and model params. It automates the process of selecting the best model parameters, given an LLM model and a prompt, iterating over the possible combinations and letting the user visually inspect the results. The tool assumes the user has Ollama installed and serving endpoints, either in `localhost` or in a remote server. Key features include: * Automatically fetches models from local or remote Ollama servers * Iterates over different models and params to generate inferences * A/B test prompts on different models simultaneously * Allows multiple iterations for each combination of parameters * Makes synchronous inference calls to avoid spamming servers * Optionally outputs inference parameters and response metadata (inference time, tokens and tokens/s) * Refetching of individual inference calls * Model selection can be filtered by name * List experiments which can be downloaded in JSON format * Configurable inference timeout * Custom default parameters and system prompts can be defined in settings

Awesome-AI-Agents is a curated list of projects, frameworks, benchmarks, platforms, and related resources focused on autonomous AI agents powered by Large Language Models (LLMs). The repository showcases a wide range of applications, multi-agent task solver projects, agent society simulations, and advanced components for building and customizing AI agents. It also includes frameworks for orchestrating role-playing, evaluating LLM-as-Agent performance, and connecting LLMs with real-world applications through platforms and APIs. Additionally, the repository features surveys, paper lists, and blogs related to LLM-based autonomous agents, making it a valuable resource for researchers, developers, and enthusiasts in the field of AI.

The Generative AI Use Cases Repository showcases a wide range of applications in generative AI, including Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. It provides practical notebooks and guidance on utilizing frameworks such as LlamaIndex and LangChain, and demonstrates how to integrate models from leading AI research companies like Anthropic and OpenAI.

A curated list of Generative AI projects, tools, artworks, and models

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

A package to help benchmark various LLM related tasks. The benchmarks are organized by end-to-end use cases, and utilize LangSmith heavily. We have several goals in open sourcing this: * Showing how we collect our benchmark datasets for each task * Showing what the benchmark datasets we use for each task is * Showing how we evaluate each task * Encouraging others to benchmark their solutions on these tasks (we are always looking for better ways of doing things!)

A comprehensive guide to building Retrieval Augmented Generation (RAG)-based LLM applications for production. This guide covers developing a RAG-based LLM application from scratch, scaling the major components, evaluating different configurations, implementing LLM hybrid routing, serving the application in a highly scalable and available manner, and sharing the impacts LLM applications have had on products.

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.

Hands-On-LLM-Applications-Development is a repository focused on developing applications using Large Language Models (LLMs). The repository provides hands-on tutorials, guides, and resources for building various applications such as LangChain for LLM applications, Retrieval Augmented Generation (RAG) with LangChain, building LLM agents with LangGraph, and advanced LangChain with OpenAI. It covers topics like prompt engineering for LLMs, building applications using HuggingFace open-source models, LLM fine-tuning, and advanced RAG applications.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

Awesome GPT Prompt Engineering is a curated list of resources, tools, and shiny things for GPT prompt engineering. It includes roadmaps, guides, techniques, prompt collections, papers, books, communities, prompt generators, Auto-GPT related tools, prompt injection information, ChatGPT plug-ins, prompt engineering job offers, and AI links directories. The repository aims to provide a comprehensive guide for prompt engineering enthusiasts, covering various aspects of working with GPT models and improving communication with AI tools.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

Advisor for safe LLM integration using OWASP guidelines

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

I automatically create and evaluate phone scripts, presenting a final draft.

Helps you evaluate whether or not tech platforms match UNESCO's Internet for Trust Guidelines for the Governance of Digital Platforms

Analyzes AFL draft data to evaluate clubs' historical performance

Generate, Evaluate, and Improve your Academic Programs

Searches the web's verified fact-checkers to evaluate facts and claims

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

I provide a structured way of using AI to support teaching and learning. I use the the CHOICE method (i.e., Clarify, Harness, Originate, Iterate, Communicate, Evaluate) to ensure that your use of AI can help you meet your educational goals.

Evaluates your long-term market position based on value offered and uniqueness against competitors.

Wertet Lebenslauf und Stellenanzeige aus und simuliert ein Vorstellungsgespräch mit anschließender Auswertung: Lebenslauf und Anzeige einfach hochladen und starten.

Evaluates link credibility, based on detailed factual analysis.

A professional assistant for technical item evaluations in banking

Simulates the IELTS Writing Test, evaluates responses, and estimates band scores.