Best AI tools for< Improve Llm Output >

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.

LLM Quality Beefer-Upper is an AI tool designed to enhance the quality and productivity of LLM responses by automating critique, reflection, and improvement. Users can generate multi-agent prompt drafts, choose from different quality levels, and upload knowledge text for processing. The application aims to maximize output quality by utilizing the best available LLM models in the market.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

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.

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.

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.

Reprompt is a prompt testing tool designed to help developers save time and make data-driven decisions about their prompts. It enables users to analyze more data in less time, easily identify anomalies, and speed up debugging by testing multiple scenarios at once. With Reprompt, users can have confidence in their changes by comparing with previous versions. The tool also offers real-time trading, < 1 sec operations, no commissions, built-in enterprise encryption and security, 256-bit AES encryption, and advanced security standards.

Private LLM is a secure, local, and private AI chatbot designed for iOS and macOS devices. It operates offline, ensuring that user data remains on the device, providing a safe and private experience. The application offers a range of features for text generation and language assistance, utilizing state-of-the-art quantization techniques to deliver high-quality on-device AI experiences without compromising privacy. Users can access a variety of open-source LLM models, integrate AI into Siri and Shortcuts, and benefit from AI language services across macOS apps. Private LLM stands out for its superior model performance and commitment to user privacy, making it a smart and secure tool for creative and productive tasks.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

Ragobble is an audio to LLM data tool that allows you to easily convert audio files into text data that can be used to train large language models (LLMs). With Ragobble, you can quickly and easily create high-quality training data for your LLM projects.

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Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.

Candor is an AI-powered team feedback platform that helps businesses improve team culture and performance. It offers a range of features including team retrospectives, check-ins, anonymous feedback, 1:1s, and 360 surveys. Candor's AI-driven insights help businesses identify and address issues within their teams, and its user-friendly interface makes it easy to set up and use. Candor is a valuable tool for any business looking to improve team communication, collaboration, and productivity.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

Tovie AI is a platform offering Generative AI and on-prem LLM solutions for enterprise applications. It provides a range of AI tools such as GenAI Agents, Data Agent, Data Mask, AI Bots, Voice Bots, Chatbots, and Sector-specific Assistants. Tovie AI aims to enhance customer service, boost employee engagement, and improve business operations through its tailored AI solutions. The platform also offers consulting services to help businesses leverage Generative AI for business optimization and growth.

DenserAI is an AI-powered chatbot application designed to be integrated into websites to enhance customer support, lead generation, and conversions. It offers powerful AI features such as intelligent search and chat systems, precise answers with source citations, advanced search functionality, and seamless integration with various platforms. DenserAI aims to provide efficient and scalable solutions for businesses to improve customer satisfaction, save operational costs, generate leads, and boost sales.

Platoria is an AI application that provides AI review summaries for products based on thousands of reviews from vendors worldwide. It condenses reviews into easily digestible summaries, offering actionable insights and boosting customer trust and sales for Shopify stores. Platoria's lightweight and snappy design works with all themes without slowing down the site, making it a valuable tool for e-commerce businesses looking to enhance their product reviews and increase sales.

Perception AI is an AI tool designed to enhance user experience by providing personalized recommendations and insights based on user behavior and preferences. The tool utilizes advanced algorithms to analyze data and generate tailored suggestions for users. With a focus on improving engagement and satisfaction, Perception AI aims to revolutionize the way users interact with digital platforms. By leveraging artificial intelligence, the tool offers a seamless and intuitive experience for users across various domains.

LangWatch is a monitoring and analytics tool for Generative AI (GenAI) solutions. It provides detailed evaluations of the faithfulness and relevancy of GenAI responses, coupled with user feedback insights. LangWatch is designed for both technical and non-technical users to collaborate and comment on improvements. With LangWatch, you can understand your users, detect issues, and improve your GenAI products.

Hebbia is an AI tool designed to help users collaborate with AI agents more confidently over all the documents that matter. It offers Matrix agents that can handle questions about millions of documents at a time, executing workflows with hundreds of steps. Hebbia is known for its Trustworthy AI approach, showing its work at each step to build user trust. The tool is used by top enterprises, financial institutions, governments, and law firms worldwide, saving users time and making them more efficient in their work.

This repository contains a library for constraining LLM generation to structured output, enforcing a JSON schema for precise data types and property names. It includes an acceptor/state machine framework, JSON acceptor, and JSON schema acceptor for guiding decoding in LLMs. The library provides reference implementations using Apple's MLX library and examples for function calling tasks. The tool aims to improve LLM output quality by ensuring adherence to a schema, reducing unnecessary output, and enhancing performance through pre-emptive decoding. Evaluations show performance benchmarks and comparisons with and without schema constraints.

A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure. Fine-Tuning retrains an existing pre-trained LLM using example data, resulting in a new 'custom' fine-tuned LLM optimized for task-specific examples. Use cases include improving LLM performance on specific tasks and introducing information not well represented by the base LLM model. Suitable for cases where latency is critical, high accuracy is required, and clear evaluation metrics are available. Learning path includes labs for fine-tuning GPT and Llama2 models via Dashboards and Python SDK.

Easily integrate Large Language Models into your Python code. Simply use the `@prompt` and `@chatprompt` decorators to create functions that return structured output from the LLM. Mix LLM queries and function calling with regular Python code to create complex logic.

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

Instructor for PHP is a library designed for structured data extraction in PHP, powered by Large Language Models (LLMs). It simplifies the process of extracting structured, validated data from unstructured text or chat sequences. Instructor enhances workflow by providing a response model, validation capabilities, and max retries for requests. It supports classes as response models and provides features like partial results, string input, extracting scalar and enum values, and specifying data models using PHP type hints or DocBlock comments. The library allows customization of validation and provides detailed event notifications during request processing. Instructor is compatible with PHP 8.2+ and leverages PHP reflection, Symfony components, and SaloonPHP for communication with LLM API providers.

CodeCompanion.nvim is a Neovim plugin that provides a Copilot Chat experience, adapter support for various LLMs, agentic workflows, inline code creation and modification, built-in actions for language prompts and error fixes, custom actions creation, async execution, and more. It supports Anthropic, Ollama, and OpenAI adapters. The plugin is primarily developed for personal workflows with no guarantees of regular updates or support. Users can customize the plugin to their needs by forking the project.

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.

optillm is an OpenAI API compatible optimizing inference proxy implementing state-of-the-art techniques to enhance accuracy and performance of LLMs, focusing on reasoning over coding, logical, and mathematical queries. By leveraging additional compute at inference time, it surpasses frontier models across diverse tasks.

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

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.

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.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

Ragtacts is a Clojure library that allows users to easily interact with Large Language Models (LLMs) such as OpenAI's GPT-4. Users can ask questions to LLMs, create question templates, call Clojure functions in natural language, and utilize vector databases for more accurate answers. Ragtacts also supports RAG (Retrieval-Augmented Generation) method for enhancing LLM output by incorporating external data. Users can use Ragtacts as a CLI tool, API server, or through a RAG Playground for interactive querying.

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.

Awesome LLM Security is a curated collection of tools, documents, and projects related to Large Language Model (LLM) security. It covers various aspects of LLM security including white-box, black-box, and backdoor attacks, defense mechanisms, platform security, and surveys. The repository provides resources for researchers and practitioners interested in understanding and safeguarding LLMs against adversarial attacks. It also includes a list of tools specifically designed for testing and enhancing LLM security.

专门优化和翻译不同LLM的提示词的专家，基于用户选择和LLM特性。

This GPT generates the best possible LLM-agents for your system prompts. You can also specify the model size, like 3B, 33B, 70B, etc.

Helps you build logic security into your GPTs custom instructions. Documentation: https://github.com/infotrix/SSLLMs---Semantic-Secuirty-for-LLM-GPTs

Gives you tips and suggestions on how you can improve your application for your users.

Upload a screenshot of your Tinder profile to improve it and get feedback

Improve your Ideas with First Principles Thinking

Offers exercises and techniques to improve memory retention and cognitive functions.

Generates conversation examples and chunks for specified situations. Improve your instantaneous conversational skills through repetitive practice!

Improve your physical & mental health

Analyzes customer churn and provides strategies to improve loyalty and retention.

Improve your curriculum telling chatgpt your cv and the desired job

Agile expert providing practical, step-by-step advice with the agile way of working of your team and organisation. Whether you're looking to improve your Agile skills or find solutions to specific problems. Including Scrum, Kanban and SAFe knowledge.

I improve objects with animals.

I improve marketing effectiveness by designing stunning research-led assets in a flash!

Helps improve language skills through interactive scenarios and feedback.

I improve Dutch sentences, explain errors, and give tips.

Bypass Internet censorship & improve your security online

Help you to improve your generative art for use in AI

Tech expert helping improve your internet speed

実践スキル向上のための対話型学習アシスタント　　　　　　　　　（Interactive learning assistant to improve practical skills）