Kamran Ahmed
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# Adding end-user IDs in prompts |
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# Adding end-user IDs in prompts |
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Sending end-user IDs in your requests can be a useful tool to help OpenAI monitor and detect abuse. This allows OpenAI to provide you with more actionable feedback in the event that they may detect any policy violations in applications. |
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Visit the following resources to learn more: |
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- [@official@OpenAI Documentation](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids) |
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# Agents Usecases |
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# Agents Usecases |
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AI Agents allow you to automate complex workflows that involve multiple steps and decisions. |
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Visit the following resources to learn more: |
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- [@article@What are AI Agents?](https://aws.amazon.com/what-is/ai-agents/) |
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- [@video@What are AI Agents?](https://www.youtube.com/watch?v=F8NKVhkZZWI) |
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# AI Agents |
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# AI Agents |
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AI Agents are a type of LLM that can be used to automate complex workflows that involve multiple steps and decisions. |
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Visit the following resources to learn more: |
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- [@article@What are AI Agents?](https://aws.amazon.com/what-is/ai-agents/) |
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- [@video@What are AI Agents?](https://www.youtube.com/watch?v=F8NKVhkZZWI) |
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# AI Agents |
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# AI Agents |
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AI Agents are a type of LLM that can be used to automate complex workflows that involve multiple steps and decisions. |
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Visit the following resources to learn more: |
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- [@article@What are AI Agents?](https://aws.amazon.com/what-is/ai-agents/) |
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- [@video@What are AI Agents?](https://www.youtube.com/watch?v=F8NKVhkZZWI) |
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# AI Code Editors |
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# AI Code Editors |
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AI code editors have the first-class support for AI in the editor. You can use AI to generate code, fix bugs, chat with your code, and more. |
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Visit the following resources to learn more: |
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- [@website@Cursor](https://cursor.com/) |
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- [@website@Zed AI](https://zed.dev/ai) |
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# AI Engineer vs ML Engineer |
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# AI Engineer vs ML Engineer |
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AI Engineer differs from an AI Researcher or ML Engineer. AI Engineers focus on leveraging pre-trained models and existing AI technologies to enhance user experiences without the need to train models from scratch. |
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# AI Safety and Ethics |
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# AI Safety and Ethics |
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Learn about the principles and guidelines for building safe and ethical AI systems. |
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# AI vs AGI |
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# AI vs AGI |
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AI (Artificial Intelligence) refers to systems designed to perform specific tasks, like image recognition or language translation, often excelling in those narrow areas. In contrast, AGI (Artificial General Intelligence) would be a system capable of understanding, learning, and applying intelligence across a wide range of tasks, much like a human, and could adapt to new situations without specific programming. |
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# Anomaly Detection |
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# Anomaly Detection |
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Embeddings transform complex data (like text or behavior) into numerical vectors, capturing relationships between data points. These vectors are stored in a vector database, which allows for efficient similarity searches. Anomalies can be detected by measuring the distance between a data point's vector and its nearest neighbors—if a point is significantly distant, it's likely anomalous. This approach is scalable, adaptable to various data types, and effective for tasks like fraud detection, predictive maintenance, and cybersecurity. |
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# Anthropic's Claude |
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# Anthropic's Claude |
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Claude is a family of large language models developed by Anthropic. Claude 3.5 Sonnet is the latest model (at the time of this writing) in the series, known for its advanced reasoning and multi-modality capabilities. |
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Visit the following resources to learn more: |
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- [@official@Clause Website](https://claude.ai/) |
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# Audio Processing |
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# Audio Processing |
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Using Multimodal AI, audio data can be processed with other types of data, such as text, images, or video, to enhance understanding and analysis. For example, it can synchronize audio with corresponding visual inputs, like lip movements in video, to improve speech recognition or emotion detection. This fusion of modalities enables more accurate transcription, better sentiment analysis, and enriched context understanding in applications such as virtual assistants, multimedia content analysis, and real-time communication systems. |
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# AWS Sagemaker |
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# AWS Sagemaker |
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AWS Sagemaker is a fully managed platform that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Sagemaker takes care of the underlying infrastructure, allowing developers to focus on building and improving their models. |
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Visit the following resources to learn more: |
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- [@official@AWS Website](https://aws.amazon.com/sagemaker/) |
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# Azure AI |
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# Azure AI |
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Azure AI is a comprehensive set of AI services and tools provided by Microsoft. It includes a range of capabilities such as natural language processing, computer vision, speech recognition, and more. Azure AI is designed to help developers and organizations build, deploy, and scale AI solutions quickly and easily. |
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Visit the following resources to learn more: |
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- [@official@Azure Website](https://azure.microsoft.com/en-us/products/ai-services/) |
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# Benefits of Pre-trained Models |
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# Benefits of Pre-trained Models |
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LLM models are not only difficult to train, but they are also expensive. Pre-trained models are a cost-effective solution for developers and organizations looking to leverage the power of AI without the need to train models from scratch. |
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Visit the following resources to learn more: |
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- [@article@Why you should use Pre-trained Models](https://cohere.com/blog/pre-trained-vs-in-house-nlp-models) |
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# Bias and Fareness |
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# Bias and Fareness |
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Bias and fairness in AI arise when models produce skewed or unequal outcomes for different groups, often reflecting imbalances in the training data. This can lead to discriminatory effects in critical areas like hiring, lending, and law enforcement. Addressing these concerns involves ensuring diverse and representative data, implementing fairness metrics, and ongoing monitoring to prevent biased outcomes. Techniques like debiasing algorithms and transparency in model development help mitigate bias and promote fairness in AI systems. |
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# Capabilities / Context Length |
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# Capabilities / Context Length |
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OpenAI's capabilities include processing complex tasks like language understanding, code generation, and problem-solving. However, context length limits how much information the model can retain and reference during a session, affecting long conversations or documents. Advances aim to increase this context window for more coherent and detailed outputs over extended interactions. |
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Visit the following resources to learn more: |
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- [@official@OpenAI Website](https://platform.openai.com/docs/guides/fine-tuning/token-limits) |
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# Chat Completions API |
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# Chat Completions API |
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The Chat Completions API allows developers to create conversational agents by sending user inputs (prompts) and receiving model-generated responses. It supports multiple-turn dialogues, maintaining context across exchanges to deliver relevant responses. This API is often used for chatbots, customer support, and interactive applications where maintaining conversation flow is essential. |
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Visit the following resources to learn more: |
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- [@official@OpenAI Website](https://platform.openai.com/docs/api-reference/chat/completions) |
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# Chroma |
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# Chroma |
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Chroma is a vector database designed to efficiently store, index, and query high-dimensional embeddings. It’s optimized for AI applications like semantic search, recommendation systems, and anomaly detection by allowing fast retrieval of similar vectors based on distance metrics (e.g., cosine similarity). Chroma enables scalable and real-time processing, making it a popular choice for projects involving embeddings from text, images, or other data types. |
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Visit the following resources to learn more: |
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- [@official@Chroma Website](https://docs.trychroma.com/) |
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# Chunking |
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# Chunking |
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In Retrieval-Augmented Generation (RAG), **chunking** refers to breaking large documents or data into smaller, manageable pieces (chunks) to improve retrieval and generation efficiency. This process helps the system retrieve relevant information more accurately by indexing these chunks in a vector database. During a query, the model retrieves relevant chunks instead of entire documents, which enhances the precision of the generated responses and allows better handling of long-form content within the context length limits. |
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# Code Completion Tools |
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# Code Completion Tools |
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AI Code Completion Tools are software tools that use AI models to assist with code generation and editing. These tools help developers write code more quickly and efficiently by providing suggestions, completing code snippets, and suggesting improvements. AI Code Completion Tools can also be used to generate documentation, comments, and other code-related content. |
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Visit the following resources to learn more: |
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- [@website@GitHub Copilot](https://copilot.github.com/) |
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- [@website@Codeium](https://codeium.com/) |
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- [@website@Supermaven](https://supermaven.com/) |
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- [@website@TabNine](https://www.tabnine.com/) |
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# Cohere |
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# Cohere |
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Cohere is an AI platform that provides natural language processing (NLP) models and tools, enabling developers to integrate powerful language understanding capabilities into their applications. It offers features like text generation, semantic search, classification, and embeddings. Cohere focuses on scalability and ease of use, making it popular for tasks such as content creation, customer support automation, and building search engines with advanced semantic understanding. It also provides a user-friendly API for custom NLP applications. |
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Visit the following resources to learn more: |
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- [@website@Cohere](https://cohere.com/) |
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# Conducting adversarial testing |
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# Conducting Adversarial Testing |
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Adversarial testing involves creating malicious inputs to test the robustness of AI models. This includes testing for prompt injection, evasion, and other adversarial attacks. |
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# Constraining outputs and inputs |
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# Constraining Outputs and Inputs |
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Constraining outputs and inputs is important for controlling the behavior of AI models. This includes techniques like output filtering, input validation, and rate limiting. |
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# Cut-off Dates / Knowledge |
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# Cut-off Dates / Knowledge |
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OpenAI models have a knowledge cutoff date, meaning they only have access to information available up until a specific time. For example, my knowledge is up to date until September 2023. As a result, I may not be aware of recent developments, events, or newly released technology. Additionally, these models don’t have real-time internet access, so they can't retrieve or update information beyond their training data. This can limit the ability to provide the latest details or react to rapidly changing topics. |
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# DALL-E API |
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# DALL-E API |
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The DALL-E API allows developers to integrate OpenAI's image generation model into their applications. Using text-based prompts, the API generates unique images that match the descriptions provided by users. This makes it useful for tasks like creative design, marketing, product prototyping, and content creation. The API is highly customizable, enabling developers to adjust parameters such as image size and style. DALL-E excels at creating visually rich content from textual descriptions, expanding the possibilities for AI-driven creative workflows. |
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