Best AI tools for< Augment Language Models >

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

Karla is an AI tool designed for journalists to enhance their writing process by utilizing Large Language Models (LLMs). It helps journalists transform news information into well-structured articles efficiently, augment their sources, customize stories seamlessly, enjoy a sleek editing experience, and export their completed stories easily. Karla acts as a wrapper around the LLM of choice, providing dynamic prompts and integration into a text editor and workflow, allowing journalists to focus on writing without manual prompt crafting. It offers benefits over traditional LLM chat apps by providing efficient prompt crafting, seamless integration, enhanced outcomes, faster performance, model flexibility, and relevant content tailored for journalism.

Veritone is a leading provider of artificial intelligence (AI) solutions for businesses. Its flagship product, aiWARE, is an enterprise AI platform that provides access to hundreds of cognitive engines through one common software infrastructure. Veritone's AI solutions are used by businesses in a variety of industries, including media and entertainment, recruitment, government, legal and compliance, and sports. Veritone's mission is to augment the human workforce by transforming use-case concepts into tangible, industry-leading applications and solutions.

PandasAI is an open-source AI tool designed for conversational data analysis. It allows users to ask questions in natural language to their enterprise data and receive real-time data insights. The tool is integrated with various data sources and offers enhanced analytics, actionable insights, detailed reports, and visual data representation. PandasAI aims to democratize data analysis for better decision-making, offering enterprise solutions for stable and scalable internal data analysis. Users can also fine-tune models, ingest universal data, structure data automatically, augment datasets, extract data from websites, and forecast trends using AI.

Prefit.AI is a generative AI search engine that enables users to quickly generate new content based on a variety of inputs. It can explore and analyze complex data in new ways, discover new trends and patterns, and summarize content, outline multiple solution paths, brainstorm ideas, and create detailed documentation from research notes. Prefit.AI can also respond naturally to human conversation and serve as a tool for customer service and personalization of customer workflows. It can augment employee workflows and act as efficient assistants for everyone in your organization.

Sana is an AI company transforming how organizations learn and access knowledge. Its AI-first learning platform and knowledge assistant are designed for people teams that want to do learning differently. The platform offers integrations, solutions for employee onboarding, sales enablement, compliance training, leadership development, and external training. The knowledge assistant helps everyone work faster, think bigger, and achieve more. Sana's products are trusted by the world's most pioneering companies.

Aide is an AI platform designed to enhance customer support operations. It offers a range of features to help businesses gain insights into customer needs, automate support processes, improve agent efficiency, and train AI chatbots. Aide's key capabilities include customer insights, workflow automation, agent assist, and AI chatbots. With Aide, businesses can analyze customer conversations, identify pain points, and automate repetitive tasks to streamline support operations and improve customer satisfaction.

SparkCognition Government Systems (SGS) is a full-spectrum artificial intelligence company dedicated to government and national defense missions. The company leverages AI technologies such as machine learning, natural language processing, and computer vision to enhance mission readiness, battle management, logistics, security, and manufacturing optimization. SparkCognition Government Systems focuses on delivering targeted AI solutions to amplify asset readiness, augment human intelligence, and accelerate decision-making processes for government organizations.

GitWit is an online tool that helps you build web apps quickly and easily, even if you don't have any coding experience. With GitWit, you can create a React app in minutes, and you can use AI to augment your own coding skills. GitWit supports React, Tailwind, and NodeJS, and it has generated over 1000 projects to date. GitWit can help you build any type of web app, from simple landing pages to complex e-commerce stores.

Arro is an AI-powered research assistant that helps product teams collect customer insights at scale. It uses automated conversations to conduct user interviews with thousands of customers simultaneously, generating product opportunities that can be directly integrated into the product roadmap. Arro's innovative AI-led methodology combines the depth of user interviews with the speed and scale of surveys, enabling product teams to gain a comprehensive understanding of their customers' needs and preferences.

Augment is a personal AI assistant that helps you remember anything, type less, and read faster. It works inside all the apps you know and love, so you can stay focused on the task at hand. Augment is designed for macOS and is trusted by professionals from all walks of life.

Tome is an AI assistant designed specifically for sales professionals. It acts as a second brain, leveraging your playbook and CRM data to help you identify strategic initiatives, key decision makers, and relevant accounts for effective outreach. With features like pinpointing important accounts, understanding company financials and competition, and crafting personalized outreach, Tome aims to streamline the sales process and enhance customer engagement.

Ideator is an AI-powered tool that helps designers and innovators generate creative ideas. It allows users to input a feature or interaction and then generates different variations of how it could be used, while keeping its main job the same. Ideator is still under development, but it has the potential to be a valuable tool for designers and innovators who are looking for new and creative ways to solve problems.

Swivl is an automation platform designed for self-storage businesses, offering intelligent automation solutions to streamline operations and enhance customer interactions. The platform leverages conversational AI technology to automate conversations with tenants, drive revenue, and augment workforce capabilities. Swivl aims to simplify the rental process, save costs, and increase revenue for self-storage operators while maintaining brand integrity. The platform is trusted by self-storage leaders for its ability to automate customer touchpoints, provide automated customer support and sales assistance, and enhance team productivity. With features like digital assistants, online self-service automation, inventory recommendations, call center deflection, and omni-channel experiences, Swivl is a comprehensive solution for self-storage businesses.

Kubiya is an AI-powered platform designed to reduce Time-To-Automation for Developer and Infrastructure Operations teams. It allows users to focus on high-impact work by automating routine tasks and processes, powered by an Agentic-Native Platform. Kubiya is trusted by world-class engineering and operations teams, offering features such as AI teammates, JIT Permissions, Infrastructure Provisioning, Help Desk support, and Incident Response automation.

Encounter AI is an automated ordering assistant designed specifically for restaurants and retail establishments. It aims to increase staff capacity and productivity by providing a robust voice solution to enhance the drive-thru experience. The AI technology enables operators to optimize existing resources, such as human capital or technology assets, to improve profitability. Encounter AI is 'kaizen' in nature, continuously learning through machine learning to stay updated on menu items and ordering preferences, ultimately enhancing the customer experience.

QuData is an AI and ML solutions provider that helps businesses enhance their value through AI/ML implementation, product design, QA, and consultancy services. They offer a range of services including ChatGPT integration, speech synthesis, speech recognition, image analysis, text analysis, predictive analytics, big data analysis, innovative research, and DevOps solutions. QuData has extensive experience in machine learning and artificial intelligence, enabling them to create high-quality solutions for specific industries, helping customers save development costs and achieve their business goals.

syntheticAIdata is a platform that provides synthetic data for training vision AI models. Synthetic data is generated artificially, and it can be used to augment existing real-world datasets or to create new datasets from scratch. syntheticAIdata's platform is easy to use, and it can be integrated with leading cloud platforms. The company's mission is to make synthetic data accessible to everyone, and to help businesses overcome the challenges of acquiring high-quality data for training their vision AI models.

The Human-Centred Artificial Intelligence Lab (Holzinger Group) is a research group focused on developing AI solutions that are explainable, trustworthy, and aligned with human values, ethical principles, and legal requirements. The lab works on projects related to machine learning, digital pathology, interactive machine learning, and more. Their mission is to combine human and computer intelligence to address pressing problems in various domains such as forestry, health informatics, and cyber-physical systems. The lab emphasizes the importance of explainable AI, human-in-the-loop interactions, and the synergy between human and machine intelligence.

Giotto.ai is a Swiss-based company focused on building Artificial General Intelligence by combining Modern Machine Learning with Topological and Algebraic methods. They offer AI Strategy Consulting, AI Application Development, AI Model Integrations, AI R&D Services, and on-demand AI & Tech Talent Solutions. The company provides services to various industries such as Insurance, Finance, Logistics, Healthcare, Education, Retail, Manufacturing, and Defence. Giotto.ai aims to automate complex processes with a high level of robustness and explainability, driving innovation in the technological space.

ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

This repository contains the source code and datasets for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs accepted to ACL 2024. It proposes a framework called Graph Chain-of-thought (Graph-CoT) to enable Language Models to traverse graphs step-by-step for reasoning, interaction, and execution. The motivation is to alleviate hallucination issues in Language Models by augmenting them with structured knowledge sources represented as graphs.

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.

This repository focuses on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). It provides code and resources to augment LLMs with real-time data for dynamic, context-aware applications. The content covers topics such as semantic search, fine-tuning embeddings, building RAG chatbots, evaluating LLMs, and using knowledge graphs in RAG. Prerequisites include Python skills, knowledge of machine learning and LLMs, and introductory experience with NLP and AI models.

Awesome RAG is a curated list of retrieval-augmented generation (RAG) in large language models. It includes papers, surveys, general resources, lectures, talks, tutorials, workshops, tools, and other collections related to retrieval-augmented generation. The repository aims to provide a comprehensive overview of the latest advancements, techniques, and applications in the field of RAG.

This repository is dedicated to collecting and categorizing papers related to Retrieval-Augmented Generation (RAG) for AI-generated content. It serves as a survey repository based on the paper 'Retrieval-Augmented Generation for AI-Generated Content: A Survey'. The repository is continuously updated to keep up with the rapid growth in the field of RAG.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

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.

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

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

The serverless-rag-demo repository showcases a solution for building a Retrieval Augmented Generation (RAG) system using Amazon Opensearch Serverless Vector DB, Amazon Bedrock, Llama2 LLM, and Falcon LLM. The solution leverages generative AI powered by large language models to generate domain-specific text outputs by incorporating external data sources. Users can augment prompts with relevant context from documents within a knowledge library, enabling the creation of AI applications without managing vector database infrastructure. The repository provides detailed instructions on deploying the RAG-based solution, including prerequisites, architecture, and step-by-step deployment process using AWS Cloudshell.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments existing causal inference processes by suggesting potential confounders, relationships between variables, backdoor sets, front door sets, IV sets, estimands, critiques of DAGs, latent confounders, and negative controls. By leveraging LLMs and formalizing human-LLM collaboration, PyWhy-LLM aims to enhance causal analysis accessibility and insight.

BambooAI is a lightweight library utilizing Large Language Models (LLMs) to provide natural language interaction capabilities, much like a research and data analysis assistant enabling conversation with your data. You can either provide your own data sets, or allow the library to locate and fetch data for you. It supports Internet searches and external API interactions.

LangCheck is a Python library that provides a suite of metrics and tools for evaluating the quality of text generated by large language models (LLMs). It includes metrics for evaluating text fluency, sentiment, toxicity, factual consistency, and more. LangCheck also provides tools for visualizing metrics, augmenting data, and writing unit tests for LLM applications. With LangCheck, you can quickly and easily assess the quality of LLM-generated text and identify areas for improvement.

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

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