Best AI tools for< Evaluate Strategies >

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.

The Startup Marketing Strategy AI Generator is an innovative tool designed to assist startups in creating effective marketing strategies. Using advanced artificial intelligence algorithms, this tool analyzes market trends, consumer behavior, and competitor data to generate personalized marketing strategies tailored to each startup's unique needs. By leveraging the power of AI, startups can save time and resources while maximizing the impact of their marketing efforts.

Reppls is an AI Interview Agents tool designed for data-driven hiring processes. It helps companies interview all applicants to identify the right talents hidden behind uninformative CVs. The tool offers seamless integration with daily tools, such as Zoom and MS Teams, and provides deep technical assessments in the early stages of hiring, allowing HR specialists to focus on evaluating soft skills. Reppls aims to transform the hiring process by saving time spent on screening, interviewing, and assessing candidates.

RebeccAi is an AI-powered business idea evaluation and validation tool that helps users assess the potential of their ideas quickly and intelligently. It offers accurate insights, fast idea refinement, and personalized business plans, revolutionizing the way entrepreneurs validate their business concepts. With over 25,000 reports generated and 5,000 users benefiting from its services, RebeccAi is a trusted platform for turning innovative ideas into successful ventures.

PandaRocket is an AI-powered suite designed to support various eCommerce business models. It offers a range of tools for product research, content creation, and store management. With features like market analysis, customer segmentation, and predictive intelligence, PandaRocket helps users make data-driven decisions to optimize their online stores and maximize profits.

RevSure is an AI-powered platform designed for high-growth marketing teams to optimize marketing ROI and attribution. It offers full-funnel attribution, deep funnel optimization, predictive insights, and campaign performance tracking. The platform integrates with various data sources to provide unified funnel reporting and personalized recommendations for improving pipeline health and conversion rates. RevSure's AI engine powers features like campaign spend reallocation, next-best touch analysis, and journey timeline construction, enabling users to make data-driven decisions and accelerate revenue growth.

InstantPersonas is an AI-powered SWOT Analysis Generator that helps organizations and individuals evaluate their Strengths, Weaknesses, Opportunities, and Threats. By using a company description, the tool generates a comprehensive SWOT Analysis, providing insights for strategic planning. Users can edit the analysis and download it as an image. InstantPersonas aims to assist in understanding target audience and market for more successful marketing strategies.

Harver is an AI-powered platform that specializes in predictive assessments and automated solutions for talent acquisition. The platform helps companies make better hiring decisions by prioritizing potential over past experiences, leading to impactful results. Harver offers a range of assessments, including personality questionnaires, cognitive ability testing, situational judgment tests, and more. By leveraging data-driven insights and intelligent automation, Harver streamlines the hiring process, reduces bias, and improves the quality of hires. The platform is designed to enhance the candidate experience, optimize sourcing investments, and provide actionable business insights for informed decision-making.

Skyline AI is an AI tool that specializes in the analysis of commercial real estate properties. It offers a platform for faster and more comprehensive evaluation of real estate investments. The tool leverages artificial intelligence to provide state-of-the-art updates on real estate and technology, enabling users to make informed decisions in the real estate sector.

Competely is an AI-powered competitive analysis tool that helps users say goodbye to tedious manual research by providing comprehensive and detailed insights into competitors across various industries. Users can save days of manual work by utilizing Competely to generate a side-by-side comparison of competitors, covering aspects such as marketing, product features, pricing, audience, customer sentiment, company info, and SWOT analysis. The tool is suitable for founders, executives, marketers, product managers, agencies, and consultants, offering valuable data-driven decisions and strategies to outsmart the competition.

WellTrade.ai is an AI-powered financial advisor tool that leverages artificial intelligence to provide clear, actionable, and data-driven investment recommendations for stocks and ETFs. It simplifies the investment process by analyzing comprehensive financial data and offering insights to help users make informed decisions. The tool aims to assist investors in navigating the complexities of stock and ETF investments by providing valuable AI-driven insights.

The Center for a New American Security (CNAS) is a bipartisan, non-profit think tank that focuses on national security and defense policy. CNAS conducts research, analysis, and policy development on a wide range of topics, including defense strategy, nuclear weapons, cybersecurity, and energy security. CNAS also provides expert commentary and analysis on current events and policy debates.

AnalyzeMe is an application that allows users to easily conduct PEST analysis. By entering industry and keywords, it provides detailed market environmental analysis. Users can use AnalyzeMe to reassess their business strategies.

EarningsCall.ai is an AI-powered platform that provides stock earnings call summaries and insights to help users turn earnings calls into a competitive advantage. The platform allows users to compare competitors, analyze trends, and make smarter business and investment decisions without the need to read full transcripts. EarningsCall.ai offers instant competitive benchmarking, market trend analysis, and use cases for business leaders, financial professionals, consultants, and advisors. The platform is designed to save time and provide valuable insights for corporate strategy, investment decisions, and financial research.

The website is an AI tool designed to assist entrepreneurs in building and growing their startups. It offers various tools powered by AI technology to evaluate business ideas, enhance branding, identify ideal customers, improve marketing strategies, and provide personalized investment plans. Users can benefit from the platform's features to make informed decisions and achieve success in their entrepreneurial endeavors.

Web3Sense is a powerful AI tool that combines on-chain data and social media analytics to provide essential insights for smarter, data-driven Web3 go-to-market and growth strategies. It helps users in audience targeting, Key Opinion Leader evaluation, and post-TGE engagement growth in the Web3 space. The platform stands out for its ability to identify micro-influencers, engage with target consumers, and make data-driven decisions to drive sustainable growth.

ParallelDots is a next-generation retail execution software powered by image recognition technology. The software offers solutions like ShelfWatch, Saarthi, and SmartGaze to enhance the efficiency of sales reps and merchandisers, provide faster training of image recognition models, and offer automated gaze-coding solutions for mobile and retail eye-tracking research. ParallelDots' computer vision technology helps CPG and retail brands track in-store compliance, address gaps in retail execution, and gain real-time insights into brand performance. The platform enables users to generate real-time KPI insights, evaluate compliance levels, convert insights into actionable strategies, and integrate computer vision with existing retail solutions seamlessly.

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.

Botify AI is an AI-powered tool designed to assist users in optimizing their website's performance and search engine rankings. By leveraging advanced algorithms and machine learning capabilities, Botify AI provides valuable insights and recommendations to improve website visibility and drive organic traffic. Users can analyze various aspects of their website, such as content quality, site structure, and keyword optimization, to enhance overall SEO strategies. With Botify AI, users can make data-driven decisions to enhance their online presence and achieve better search engine results.

Prelaunch.com is an AI-powered platform that provides bullet-proof insights from ready-to-buy customers to help businesses explore products, test concepts, evaluate pricing, iterate market positioning, and more. It offers features such as a performance dashboard to track data points, surveys for immediate insights, AI idea validation, AI market research, and next-gen focus groups. The platform helps in understanding market competitors, creating customer personas, and conducting detailed project lifecycle control. With a 1M+ community and trusted by 1000+ brands, Prelaunch.com is an all-in-one platform for launching iconic products.

Sports-betting is a Python library for implementing betting strategies and analyzing sports data. It provides tools for collecting, processing, and visualizing sports data to make informed betting decisions. The library includes modules for scraping data from sports websites, calculating odds, simulating betting strategies, and evaluating performance. With sports-betting, users can automate betting processes, test different strategies, and improve their betting outcomes.

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.

FinRL-DeepSeek is a project focusing on LLM-infused risk-sensitive reinforcement learning for trading agents. It provides a framework for training and evaluating trading agents in different market conditions using deep reinforcement learning techniques. The project integrates sentiment analysis and risk assessment to enhance trading strategies in both bull and bear markets. Users can preprocess financial news data, add LLM signals, and train agent-ready datasets for PPO and CPPO algorithms. The project offers specific training and evaluation environments for different agent configurations, along with detailed instructions for installation and usage.

Large Language Models Play StarCraft II is a project that explores the capabilities of large language models (LLMs) in playing the game StarCraft II. The project introduces TextStarCraft II, a textual environment for the game, and a Chain of Summarization method for analyzing game information and making strategic decisions. Through experiments, the project demonstrates that LLM agents can defeat the built-in AI at a challenging difficulty level. The project provides benchmarks and a summarization approach to enhance strategic planning and interpretability in StarCraft II gameplay.

OpenPromptInjection is an open-source toolkit for attacks and defenses in LLM-integrated applications, enabling easy implementation, evaluation, and extension of attacks, defenses, and LLMs. It supports various attack and defense strategies, including prompt injection, paraphrasing, retokenization, data prompt isolation, instructional prevention, sandwich prevention, perplexity-based detection, LLM-based detection, response-based detection, and know-answer detection. Users can create models, tasks, and apps to evaluate different scenarios. The toolkit currently supports PaLM2 and provides a demo for querying models with prompts. Users can also evaluate ASV for different scenarios by injecting tasks and querying models with attacked data prompts.

RouteLLM is a framework for serving and evaluating LLM routers. It allows users to launch an OpenAI-compatible API that routes requests to the best model based on cost thresholds. Trained routers are provided to reduce costs while maintaining performance. Users can easily extend the framework, compare router performance, and calibrate cost thresholds. RouteLLM supports multiple routing strategies and benchmarks, offering a lightweight server and evaluation framework. It enables users to evaluate routers on benchmarks, calibrate thresholds, and modify model pairs. Contributions for adding new routers and benchmarks are welcome.

MachineSoM is a code repository for the paper 'Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View'. It focuses on the emergence of intelligence from collaborative and communicative computational modules, enabling effective completion of complex tasks. The repository includes code for societies of LLM agents with different traits, collaboration processes such as debate and self-reflection, and interaction strategies for determining when and with whom to interact. It provides a coding framework compatible with various inference services like Replicate, OpenAI, Dashscope, and Anyscale, supporting models like Qwen and GPT. Users can run experiments, evaluate results, and draw figures based on the paper's content, with available datasets for MMLU, Math, and Chess Move Validity.

LLM-as-a-Judge is a repository that includes papers discussed in a survey paper titled 'A Survey on LLM-as-a-Judge'. The repository covers various aspects of using Large Language Models (LLMs) as judges for tasks such as evaluation, reasoning, and decision-making. It provides insights into evaluation pipelines, improvement strategies, and specific tasks related to LLMs. The papers included in the repository explore different methodologies, applications, and future research directions for leveraging LLMs as evaluators in various domains.

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in. Ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

MMLU-Pro is an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. It integrates more challenging, reasoning-focused questions and increases answer choices per question, significantly raising difficulty. The dataset comprises over 12,000 questions from academic exams and textbooks across 14 diverse domains. Experimental results show a significant drop in accuracy compared to the original MMLU, with greater stability under varying prompts. Models utilizing Chain of Thought reasoning achieved better performance on MMLU-Pro.

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.

Do-Not-Answer is an open-source dataset curated to evaluate Large Language Models' safety mechanisms at a low cost. It consists of prompts to which responsible language models do not answer. The dataset includes human annotations and model-based evaluation using a fine-tuned BERT-like evaluator. The dataset covers 61 specific harms and collects 939 instructions across five risk areas and 12 harm types. Response assessment is done for six models, categorizing responses into harmfulness and action categories. Both human and automatic evaluations show the safety of models across different risk areas. The dataset also includes a Chinese version with 1,014 questions for evaluating Chinese LLMs' risk perception and sensitivity to specific words and phrases.

Moonshot is a simple and modular tool developed by the AI Verify Foundation to evaluate Language Model Models (LLMs) and LLM applications. It brings Benchmarking and Red-Teaming together to assist AI developers, compliance teams, and AI system owners in assessing LLM performance. Moonshot can be accessed through various interfaces including User-friendly Web UI, Interactive Command Line Interface, and seamless integration into MLOps workflows via Library APIs or Web APIs. It offers features like benchmarking LLMs from popular model providers, running relevant tests, creating custom cookbooks and recipes, and automating Red Teaming to identify vulnerabilities in AI systems.

Easy-Translate is a script designed for translating large text files with a single command. It supports various models like M2M100, NLLB200, SeamlessM4T, LLaMA, and Bloom. The tool is beginner-friendly and offers seamless and customizable features for advanced users. It allows acceleration on CPU, multi-CPU, GPU, multi-GPU, and TPU, with support for different precisions and decoding strategies. Easy-Translate also provides an evaluation script for translations. Built on HuggingFace's Transformers and Accelerate library, it supports prompt usage and loading huge models efficiently.

The LLM Prompt Tuning Playbook is a comprehensive guide for improving the performance of post-trained Language Models (LLMs) through effective prompting strategies. It covers topics such as pre-training vs. post-training, considerations for prompting, a rudimentary style guide for prompts, and a procedure for iterating on new system instructions. The playbook emphasizes the importance of clear, concise, and explicit instructions to guide LLMs in generating desired outputs. It also highlights the iterative nature of prompt development and the need for systematic evaluation of model responses.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework consists of MDP formulation, RICO algorithm with rollout and update stages, and reward normalization strategies to stabilize training. RAGEN aims to optimize reasoning and action strategies for LLM agents operating in complex environments.

RAID is the largest and most comprehensive dataset for evaluating AI-generated text detectors. It contains over 10 million documents spanning 11 LLMs, 11 genres, 4 decoding strategies, and 12 adversarial attacks. RAID is designed to be the go-to location for trustworthy third-party evaluation of popular detectors. The dataset covers diverse models, domains, sampling strategies, and attacks, making it a valuable resource for training detectors, evaluating generalization, protecting against adversaries, and comparing to state-of-the-art models from academia and industry.

LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

Prompt Engineering Techniques is a comprehensive repository for learning, building, and sharing prompt engineering techniques, from basic concepts to advanced strategies for leveraging large language models. It provides step-by-step tutorials, practical implementations, and a platform for showcasing innovative prompt engineering techniques. The repository covers fundamental concepts, core techniques, advanced strategies, optimization and refinement, specialized applications, and advanced applications in prompt engineering.

OpenManus-RL is an open-source initiative focused on enhancing reasoning and decision-making capabilities of large language models (LLMs) through advanced reinforcement learning (RL)-based agent tuning. The project explores novel algorithmic structures, diverse reasoning paradigms, sophisticated reward strategies, and extensive benchmark environments. It aims to push the boundaries of agent reasoning and tool integration by integrating insights from leading RL tuning frameworks and continuously updating progress in a dynamic, live-streaming fashion.

Automated AI experiment tool for evaluating prompt strategies.

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Adapts communication style for climate strategies based on dialogue context.

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Interactive questionnaire-based guide for assessing tax residency, liability, and legal minimization strategies.

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Advisor for safe LLM integration using OWASP guidelines

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Expert on Strategic Management and Globalization

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