Griptape AI Glossary

AI Agents: AI-driven entities that perform specific tasks like customer support or data analysis, improving efficiency and accuracy. AI Agents are unique because they don't just answer questions but can perform actions, like order a part or close an account.

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AI Framework: The tools, libraries, and guidelines that help developers build, train, and deploy artificial intelligence (AI) models. These frameworks simplify development by providing pre-built functions for everyday tasks like data processing or model training. AI frameworks support machine learning tasks, including natural language processing, computer vision, and predictive analytics. Popular AI frameworks like TensorFlow, PyTorch, and (of course) Griptape provide modular, scalable environments, allowing developers to focus on business logic without handling low-level coding complexities.

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API Integration: The connection of AI with external applications and systems through Application Programming Interfaces (APIs) to share and leverage data effectively. APIs help developers make better apps and let complex organizations integrate their data in real-time.

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Automation: Using AI to perform repetitive tasks without manual intervention, increasing operational efficiency. Needless to say, all business processes should be re-examined in light of what AI can do.

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Composable Workflows: These are modular components that can be easily combined or adjusted for flexibility in building AI systems. This makes life easier for developers, so they don't have to "reinvent the wheel" when creating new systems.

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Conversational AI:  "Hey, Alexa, can you play some smooth jazz?" Conversational AI is just that - it's a technology that enables machines to understand, process, and respond to human language naturally and engagingly. It powers virtual assistants, chatbots, and voice-based applications to facilitate interactions via text or speech. Using natural language processing (NLP), machine learning, and speech recognition, conversational AI can simulate honest human conversations, answer questions, provide customer service, and even assist with complex tasks.

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Data Governance: Policies and practices ensuring proper management, security, and integrity of data used by AI systems. AI needs good data governance to be effective.

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ETL (Extract, Transform, Load): ‍

A process that prepares data for AI by extracting, cleaning, and organizing it for analysis. ETL used to be an arduous process, but AI is helping a lot. AI enhances ETL processes by automating data extraction, transformation, and cleaning while optimizing performance and scalability. It also improves data accuracy with error detection, ensures compliance, and provides predictive insights for better decision-making.

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Generative Pre-trained Transformer (GPT): 

An AI model that generates human-like text. It uses a deep-learning architecture called aTransformer (see below), which excels at processing data sequences. GPT models are pre-trained on vast amounts of text data, learning to predict the next word in a sequence. After pre-training, they can be fine-tuned for tasks like translation, summarization, or conversation. GPT models are known for generating coherent and contextually relevant responses based on input prompts.

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Generator:

‍A component of a model, particularly in Generative Adversarial Networks (GANs), responsible for creating new data instances. The generator tries to produce data (like images, text, or other outputs) indistinguishable from real-world data. It takes random input (often noise) into plausible outputs. The generator aims to improve its outputs over time as it competes against a discriminator (another component in GANs), which evaluates whether the generated data is real or fake.

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Griptape AI:

‍An awesome platform for building, deploying, and managing AI applications to streamline data processing and integration.

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Grounding:

The process of connecting abstract concepts or representations AI systems use to real-world objects, experiences, or contexts. It ensures that when an AI model, such as a conversational agent or a generative model, interacts with the world or users, its responses or outputs are relevant and meaningful in a real-world setting. Grounding allows AI systems to better understand and process language or data in a way that aligns with human understanding and context.

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Hallucination:

‍Refers to a situation where the AI generates information or responses that are incorrect, fabricated, or irrelevant to the context. This is common in large language models like GPT when they produce content that sounds plausible but is factually wrong or unsupported by the data they've been trained on. AI hallucinations can occur due to the model's attempt to generate coherent responses based on patterns in training data, even when there's no accurate basis for the specific output.

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Human in the Loop (HITL):

Refers to a method in artificial intelligence and machine learning where human intervention is integrated into the model training, decision-making, or evaluation process. In HITL systems, humans review, guide, or correct AI outputs, ensuring the model learns correctly and improves accuracy. This approach is instrumental in complex or high-stakes applications, as human oversight helps prevent errors, biases, or undesirable outcomes, making the AI system more reliable and aligned with human judgment.

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Large language model (LLM):

‍A type of artificial intelligence model designed for natural language processing tasks, characterized by its extensive scale, often containing billions of parameters. LLMs are trained on vast corpora of text data to predict and generate human-like text, understand context, and perform a wide array of language-related tasks, from text generation and translation to answering questions and summarizing documents. They leverage transformer architectures to process and generate language, enhancing their ability to handle complex linguistic structures and nuances.

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Machine Learning (ML):

A subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.

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Model:

‍A mathematical framework or algorithm trained to recognize patterns, make predictions, or carry out specific tasks based on data. The model is created during the training phase, where it learns from large datasets and adjusts its internal parameters to minimize errors. Once trained, the model can be used for classification, regression, or generating content. Examples of AI models include neural networks, decision trees, and support vector machines.

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Natural Language Processing (NLP):

The AI-driven ability to understand, interpret, and respond to human language, often used in customer service chatbots and support systems.

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Off-Prompt:

A security feature that processes sensitive data outside of AI prompts to ensure safety and reduce latency. Griptape handles "off-prompt" scenarios using specialized drivers and tools designed for modular AI workflows. It offers a structured approach to managing external resources such as web scraping or text summarization. 

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Predictable Pipelines:

‍Structured workflows that ensure reliable, sequential task management for consistency.

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Prompt engineering:

‍Designing and refining the input prompts given to language models (such as GPT) to get the desired output. Since AI models generate responses based on the context provided by the input, the way questions or tasks are framed can significantly impact the quality and relevance of the results. Prompt engineering involves crafting these inputs effectively to improve the accuracy, creativity, or usefulness of the AI-generated responses, especially in tasks like text generation, translation, or summarization.

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Retrieval as a Service (RAG):

‍Technology that retrieves data on-demand from databases to deliver relevant, AI-driven responses. This is really important for AI systems to gain context for their tasks. 

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Schema Validation:

‍A security measure that checks data against defined structures, ensuring validity in AI systems. For example, in an XML or JSON file, schema validation ensures that the data adheres to a standard structure (like an XML Schema Definition or JSON Schema), allowing applications to process the data correctly.

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Structure Runtime (RUN):

Griptape's environment for running AI agents and workflows in real-time within client applications.

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Technology-Agnostic:

‍Designed to work seamlessly with various software, data stores, and AI models for compatibility.

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Transformer:

‍A Transformer is a type of neural network architecture primarily used in natural language processing (NLP) that relies on self-attention, or simply attention, to weigh the importance of each part of the input data. Unlike traditional recurrent neural networks (RNNs), Transformers process input sequences simultaneously, allowing for parallelization and, thus, faster computation. They typically consist of an encoder-decoder structure where the encoder reads the input sequence and the decoder produces the output sequence, with layers that include multiple attention heads to focus on different data representations. Transformers have revolutionized fields like machine translation, text generation, and more by effectively handling long-range dependencies in data without the sequential processing constraints of RNNs.

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Transparency for Debugging:

A methodology within AI development where the internal operations, decision-making processes, and data flows of AI systems are designed to be transparent, interpretable, and accessible. This transparency facilitates the identification, diagnosis, and correction of errors, biases, or unintended behaviors, enhancing the debuggability and trustworthiness of AI systems.

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Transparency for Security:

A principle in AI design where AI systems' inner workings, data handling, and decision-making processes are made visible and understandable to ensure they operate securely. This transparency helps detect vulnerabilities, mitigate risks, and verify that security measures are effectively implemented, thus enhancing the robustness and trustworthiness of AI against potential threats or manipulations.

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‍Web Scraping:

This uses automated tools to extract data from websites, which can be used for various purposes such as analysis, reporting, or feeding into other applications. In AI-driven frameworks like Griptape, web scraping is integrated into workflows to automate data extraction and further processing. Griptape's WebScraperTool, for instance, allows agents to scrape content from specified web pages, making it accessible for downstream tasks such as summarization or data transformation.