Best AI tools for< Improve Scores >

PrepGenius.ai is an AI-driven test preparation platform designed to revolutionize the way students prepare for AP courses, college admission tests, and more. The platform offers personalized study plans, real-time feedback, interactive learning tools, and comprehensive resources to help students understand their strengths and weaknesses. With PrepGenius.ai, students can study smarter, receive tailored feedback, and track their progress to improve their test scores effectively.

Hubtype is an enterprise chatbot platform that offers customer service automation solutions beyond text-only AI. It provides a messaging-first helpdesk, a drag-and-drop flow builder, and a customer service analytics suite to help businesses create interactive and efficient automated customer experiences. With end-to-end encryption, secure SSL Webhooks, and seamless integration with existing technologies, Hubtype aims to enhance customer satisfaction and drive sales conversions.

Balto is a real-time guidance platform designed for contact centers, powered by AI technology. It offers features such as real-time coaching, QA copilot, notetaking automation, and integrations with various CCaaS systems. Balto aims to improve agent performance, enhance customer experience, ensure compliance, and drive sales growth through behavior change and perfect conversations on every call.

ioni.ai is an AI application that offers ChatGPT-4 solution for customer support. It is a smart chatbot based on the latest AI technology, designed to handle general inquiries, complex questions, and user-specific requests. The application streamlines workflow with immediate responses, brings CSAT scores to a new level, and ensures human-in-the-loop verification for quality control. With self-learning capabilities, ioni.ai constantly improves its responses and provides accurate solutions to customer inquiries.

Sapien ai is an AI-powered tool that helps users transform their notes into powerful quizzes. By simply uploading notes in pdf, docx, or txt format, the tool generates custom quizzes to aid in exam preparation and enhance the learning experience. With Sapien ai, studying becomes more interactive and efficient, making it easier for users to grasp and retain information.

SmartDispute.ai is an AI-powered credit repair application that helps users improve their credit scores by leveraging artificial intelligence technology. The application offers a patented Smart Dispute System™ technology that identifies and helps remove negative accounts from credit reports, making it one of the most effective credit repair systems available. Users can easily repair their own credit with the help of SmartDispute.ai's automated processes and fact-based dispute method. The application provides users with a simple and clearly explained process, allowing them to track their progress and see significant improvements in their credit scores over time.

Yogger is a video analysis app and AI movement screening tool that enables users to analyze movement anytime, anywhere. The technology allows for motion capture on mobile devices, making it easy to improve performance, prevent injuries, and achieve personal bests effortlessly. With Yogger, users can perform multiple movements, gather information instantly, and receive detailed reports on movement screenings. It is a motivational tool for clients looking to improve their assessment scores and a convenient way for trainers and coaches to assess clients and communicate ways to enhance performance.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

Convr is a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification and risk scoring. As a trusted technology partner and advisor with deep industry expertise, we help insurance organizations transform their underwriting operations through our AI-driven digital underwriting analysis platform.

How Attractive Am I is an AI-powered tool that analyzes facial features to calculate an attractiveness score. By evaluating symmetry and proportions, the tool provides personalized beauty scores. Users can upload a photo to discover their true beauty potential. The tool ensures accuracy by providing guidelines for taking photos and offers a fun and insightful way to understand facial appeal.

Pitchleague.ai is an AI-powered pitch deck coach that provides slide-by-slide feedback on how to improve your startup's pitch deck. It also scores your deck's investability and allows you to compare your deck to other startups. Pitchleague.ai is used by over 3000 founders and has helped many startups to improve their fundraising success.

InStore.ai is an AI-powered tool designed to monitor, compare, and elevate customer experience across stores. It helps businesses improve store performance by providing key performance scores, proactive guidance, and instant search capabilities to summarize in-store interactions and trends. The tool offers solutions for various industries like fuel & convenience, hospitality, and luxury retail, enabling businesses to understand customer feedback, optimize service, and refine customer interactions. InStore.ai leverages AI to enhance face-to-face experiences for customers and employees, providing timely insights, detailed support, and configurable recommendations tailored to specific audiences.

Pitch Patterns is an AI-powered Quality Control platform designed for teams analyzing sales and customer service calls to enhance close rates and CSAT scores. The platform offers innovative features such as Social Skill Markers, Conversation Analysis, and AI Tracking to provide valuable insights and improve agent performance. With integration capabilities with popular CRM systems like Salesforce and Pipedrive, Pitch Patterns aims to revolutionize call center excellence through AI analytics.

PDF2Quiz is an AI-powered tool that allows users to convert PDF documents into interactive quizzes. Users can upload a PDF, specify the number of questions, select the language, and set the difficulty level to transform the PDF into an engaging quiz. The tool utilizes Optical Character Recognition (OCR) to create quizzes from PDFs with non-selectable text, making it easy for users to assess their knowledge and share quizzes with others. With multilingual quiz conversion capabilities, PDF2Quiz caters to users from various linguistic backgrounds. The tool also offers features such as reviewing scores and answers, challenging users with automatically generated multiple-choice questions, and enabling offline use by saving quizzes and answers as PDFs.

CogniCircuit AI is an AI-powered TOEFL preparation application designed to help students improve their English skills and achieve high scores in the TOEFL exam. The app offers comprehensive practice tests, personalized feedback, and realistic exam simulations to enhance students' reading, speaking, writing, and listening abilities. With over 90,000 students on the platform and a success rate of 98%, CogniCircuit AI is a trusted tool for TOEFL test preparation.

Amori is an AI-powered dating coach application that analyzes text conversations to provide relationship insights and compatibility scores. It uses a GPT-powered algorithm to offer sassy reports on relationship dynamics, attachment styles, communication patterns, intimacy moments, challenges, and more. The app, founded at Stanford, has been featured in known publications like the Wall Street Journal, BuzzFeed, Cosmopolitan, CNBC, and Business Insider. Amori aims to help users understand and improve their relationships by offering personalized analyses and advice based on their text interactions.

VisualHUB is an AI-powered design analysis tool that provides instant insights on UI, UX, readability, and more. It offers features like A/B Testing, UI Analysis, UX Analysis, Readability Analysis, Margin and Hierarchy Analysis, and Competition Analysis. Users can upload product images to receive detailed reports with actionable insights and scores. Trusted by founders and designers, VisualHUB helps optimize design variations and identify areas for improvement in products.

VisualEyes is a user experience (UX) optimization tool that uses attention heatmaps and clarity scores to help businesses improve the effectiveness of their digital products. It provides insights into how users interact with websites and applications, allowing businesses to identify areas for improvement and make data-driven decisions about their designs. VisualEyes is part of Neurons, a leading neuroscience company that specializes in providing AI-powered solutions for businesses.

SWMS AI is an AI-powered safety risk assessment tool that helps businesses streamline compliance and improve safety. It leverages a vast knowledge base of occupational safety resources, codes of practice, risk assessments, and safety documents to generate risk assessments tailored specifically to a project, trade, and industry. SWMS AI can be customized to a company's policies to align its AI's document generation capabilities with proprietary safety standards and requirements.

Moning is a platform designed to help users manage and boost their wealth easily. It provides tools for a global view of wealth, making better investment decisions, avoiding costly mistakes, and increasing performance. With features like AI Analysis, Dividends calendar, and Dividend and Growth Safety Scores, Moning offers a mix of Human & Artificial Intelligence to enhance investment knowledge and decision-making. Users can track and manage their wealth through a comprehensive dashboard, access detailed information on stocks, ETFs, and cryptos, and benefit from quick screeners to find the best investment opportunities.

FeedGen is an open-source tool that uses Google Cloud's state-of-the-art Large Language Models (LLMs) to improve product titles, generate more comprehensive descriptions, and fill missing attributes in product feeds. It helps merchants and advertisers surface and fix quality issues in their feeds using Generative AI in a simple and configurable way. The tool relies on GCP's Vertex AI API to provide both zero-shot and few-shot inference capabilities on GCP's foundational LLMs. With few-shot prompting, users can customize the model's responses towards their own data, achieving higher quality and more consistent output. FeedGen is an Apps Script based application that runs as an HTML sidebar in Google Sheets, allowing users to optimize their feeds with ease.

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.

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.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

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.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

R-Judge is a benchmarking tool designed to evaluate the proficiency of Large Language Models (LLMs) in judging and identifying safety risks within diverse environments. It comprises 569 records of multi-turn agent interactions, covering 27 key risk scenarios across 5 application categories and 10 risk types. The tool provides high-quality curation with annotated safety labels and risk descriptions. Evaluation of 11 LLMs on R-Judge reveals the need for enhancing risk awareness in LLMs, especially in open agent scenarios. Fine-tuning on safety judgment is found to significantly improve model performance.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

RAGoon is a high-level library designed for batched embeddings generation, fast web-based RAG (Retrieval-Augmented Generation) processing, and quantized indexes processing. It provides NLP utilities for multi-model embedding production, high-dimensional vector visualization, and enhancing language model performance through search-based querying, web scraping, and data augmentation techniques.

Examor is a website application that allows you to take exams based on your knowledge notes. It helps you to remember what you have learned and written. The application generates a set of questions from the documents you upload, and you can answer them to test your knowledge. Examor also uses GPT to score and validate your answers, and provides you with feedback. The application is still in its early stages of development, but it has the potential to be a valuable tool for learners.

STRIDE GPT is an AI-powered threat modelling tool that leverages Large Language Models (LLMs) to generate threat models and attack trees for a given application based on the STRIDE methodology. Users provide application details, such as the application type, authentication methods, and whether the application is internet-facing or processes sensitive data. The model then generates its output based on the provided information. It features a simple and user-friendly interface, supports multi-modal threat modelling, generates attack trees, suggests possible mitigations for identified threats, and does not store application details. STRIDE GPT can be accessed via OpenAI API, Azure OpenAI Service, Google AI API, or Mistral API. It is available as a Docker container image for easy deployment.

Fastc is a tool focused on CPU execution, using efficient models for embedding generation and cosine similarity classification. It allows for efficient multi-classifier execution without extra overhead. Users can easily train text classifiers, export models, publish to HuggingFace, load existing models, make class predictions, use instruct templates, and launch an inference server. The tool provides an HTTP API for text classification with JSON payloads and supports multiple languages for language identification.

Tonic Validate is a framework for the evaluation of LLM outputs, such as Retrieval Augmented Generation (RAG) pipelines. Validate makes it easy to evaluate, track, and monitor your LLM and RAG applications. Validate allows you to evaluate your LLM outputs through the use of our provided metrics which measure everything from answer correctness to LLM hallucination. Additionally, Validate has an optional UI to visualize your evaluation results for easy tracking and monitoring.

Black Marlin is a UCI compliant chess engine fully written in Rust by Doruk Sekercioglu. It supports Chess960 and features a variety of search algorithms, pruning techniques, and evaluation methods. Black Marlin is designed to be efficient and accurate, and it has been shown to perform well against other top chess engines.

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