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# AI Agents
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In AI engineering, "agents" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.
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Visit the following resources to learn more:
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- [@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/)
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- [@article@AI Agents and Their Types](https://www.digitalocean.com/resources/articles/types-of-ai-agents)
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- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
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# AI Agents
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In AI engineering, "agents" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.
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Visit the following resources to learn more:
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- [@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/)
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- [@article@AI agents and their types](https://play.ht/blog/ai-agents-use-cases/)
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- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
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# AI Code Editors
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AI code editors are development tools that leverage artificial intelligence to assist software developers in writing, debugging, and optimizing code. These editors go beyond traditional syntax highlighting and code completion by incorporating machine learning models, natural language processing, and data analysis to understand code context, generate suggestions, and even automate portions of the software development process.
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Visit the following resources to learn more:
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- [@official@Gemini CLI - Google's AI coding assistant for command line](https://github.com/google-gemini/gemini-cli)
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- [@official@OpenAI Codex - AI code generation via API and CLI](https://openai.com/codex/)
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- [@article@Cursor - The AI Code Editor](https://www.cursor.com/)
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- [@article@PearAI - The Open Source, Extendable AI Code Editor](https://trypear.ai/)
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- [@article@Bolt - Prompt, run, edit, and deploy full-stack web apps](https://bolt.new)
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- [@article@Replit - Build Apps using AI](https://replit.com/ai)
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- [@article@v0 - Build Apps with AI](https://v0.dev)
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- [@article@Claude Code - AI coding assistant in terminal](https://www.claude.com/product/claude-code)
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# Anthropic's Claude
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Anthropic's Claude is an AI language model designed to facilitate safe and scalable AI systems. Named after Claude Shannon, the father of information theory, Claude focuses on responsible AI use, emphasizing safety, alignment with human intentions, and minimizing harmful outputs. Built as a competitor to models like OpenAI's GPT, Claude is designed to handle natural language tasks such as generating text, answering questions, and supporting conversations, with a strong focus on aligning AI behavior with user goals while maintaining transparency and avoiding harmful biases.
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Visit the following resources to learn more:
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- [@official@Claude](https://claude.ai)
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- [@video@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
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# AWS SageMaker
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AWS SageMaker is a fully managed machine learning service from Amazon Web Services that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides an integrated development environment, simplifying the entire ML workflow, from data preparation and model development to training, tuning, and inference. SageMaker supports popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn, and offers features like automated model tuning, model monitoring, and one-click deployment. It's designed to make machine learning more accessible and scalable, even for large enterprise applications.
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Visit the following resources to learn more:
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- [@official@AWS SageMaker](https://aws.amazon.com/sagemaker/)
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- [@video@Introduction to Amazon SageMaker](https://www.youtube.com/watch?v=Qv_Tr_BCFCQ)
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# Azure AI
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Azure AI is a suite of AI services and tools provided by Microsoft through its Azure cloud platform. It includes pre-built AI models for natural language processing, computer vision, and speech, as well as tools for developing custom machine learning models using services like Azure Machine Learning. Azure AI enables developers to integrate AI capabilities into applications with APIs for tasks like sentiment analysis, image recognition, and language translation. It also supports responsible AI development with features for model monitoring, explainability, and fairness, aiming to make AI accessible, scalable, and secure across industries.
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Visit the following resources to learn more:
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- [@official@Azure AI](https://azure.microsoft.com/en-gb/solutions/ai)
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- [@video@How to Choose the Right Models for Your Apps](https://www.youtube.com/watch?v=sx_uGylH8eg)
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# Benefits of Pre-trained Models
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Pre-trained models offer several benefits in AI engineering by significantly reducing development time and computational resources because these models are trained on large datasets and can be fine-tuned for specific tasks, which enables quicker deployment and better performance with less data. They help overcome the challenge of needing vast amounts of labeled data and computational power for training from scratch. Additionally, pre-trained models often demonstrate improved accuracy, generalization, and robustness across different tasks, making them ideal for applications in natural language processing, computer vision, and other AI domains.
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Visit the following resources to learn more:
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- [@article@Why Pre-Trained Models Matter For Machine Learning](https://www.ahead.com/resources/why-pre-trained-models-matter-for-machine-learning/)
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- [@article@Why You Should Use Pre-Trained Models Versus Building Your Own](https://cohere.com/blog/pre-trained-vs-in-house-nlp-models)
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# Capabilities / Context Length
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A key aspect of the OpenAI models is their context length, which refers to the amount of input text the model can process at once. Earlier models like GPT-3 had a context length of up to 4,096 tokens (words or word pieces), while more recent models like GPT-4 can handle significantly larger context lengths, some supporting up to 32,768 tokens. This extended context length enables the models to handle more complex tasks, such as maintaining long conversations or processing lengthy documents, which enhances their utility in real-world applications like legal document analysis or code generation.
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Visit the following resources to learn more:
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- [@official@Managing Context](https://platform.openai.com/docs/guides/conversation-state?api-mode=responses#managing-context-for-text-generation)
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- [@official@Capabilities](https://platform.openai.com/docs/guides/text-generation)
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# Chat Completions API
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The OpenAI Chat Completions API is a powerful interface that allows developers to integrate conversational AI into applications by utilizing models like GPT-3.5 and GPT-4. It is designed to manage multi-turn conversations, keeping context across interactions, making it ideal for chatbots, virtual assistants, and interactive AI systems. With the API, users can structure conversations by providing messages in a specific format, where each message has a role (e.g., "system" to guide the model, "user" for input, and "assistant" for responses).
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Visit the following resources to learn more:
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- [@official@Create Chat Completions](https://platform.openai.com/docs/api-reference/chat/create)
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- [@article@Getting Start with Chat Completions API](https://medium.com/the-ai-archives/getting-started-with-openais-chat-completions-api-in-2024-462aae00bf0a)
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# Code Completion Tools
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Code completion tools are AI-powered development assistants designed to enhance productivity by automatically suggesting code snippets, functions, and entire blocks of code as developers type. These tools, such as GitHub Copilot and Tabnine, leverage machine learning models trained on vast code repositories to predict and generate contextually relevant code. They help reduce repetitive coding tasks, minimize errors, and accelerate the development process by offering real-time, intelligent suggestions.
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Visit the following resources to learn more:
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- [@official@GitHub Copilot](https://github.com/features/copilot)
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- [@official@Codeium](https://codeium.com/)
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- [@official@Supermaven](https://supermaven.com/)
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- [@official@Tabnine](https://www.tabnine.com/)
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# Cut-off Dates / Knowledge
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OpenAI models, such as GPT-3.5 and GPT-4, have a knowledge cutoff date, which refers to the last point in time when the model was trained on data. For instance, as of the current version of GPT-4, the knowledge cutoff is October 2023. This means the model does not have awareness or knowledge of events, advancements, or data that occurred after that date. Consequently, the model may lack information on more recent developments, research, or real-time events unless explicitly updated in future versions. This limitation is important to consider when using the models for time-sensitive tasks or inquiries involving recent knowledge.
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Visit the following resources to learn more:
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- [@article@Knowledge Cutoff Dates of all LLMs explained](https://otterly.ai/blog/knowledge-cutoff/)
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- [@article@Knowledge Cutoff Dates For ChatGPT, Meta Ai, Copilot, Gemini, Claude](https://computercity.com/artificial-intelligence/knowledge-cutoff-dates-llms)
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# Fine-tuning
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Fine-tuning the OpenAI API involves adapting pre-trained models, such as GPT, to specific use cases by training them on custom datasets. This process allows you to refine the model's behavior and improve its performance on specialized tasks, like generating domain-specific text or following particular patterns. By providing labeled examples of the desired input-output pairs, you guide the model to better understand and predict the appropriate responses for your use case.
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Visit the following resources to learn more:
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- [@official@Fine-tuning Documentation](https://platform.openai.com/docs/guides/fine-tuning)
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- [@video@Fine-tuning ChatGPT with OpenAI Tutorial](https://www.youtube.com/watch?v=VVKcSf6r3CM)
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# Google's Gemini
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Google Gemini is an advanced AI model by Google DeepMind, designed to integrate natural language processing with multimodal capabilities, enabling it to understand and generate not just text but also images, videos, and other data types. It combines generative AI with reasoning skills, making it effective for complex tasks requiring logical analysis and contextual understanding. Built on Google's extensive knowledge base and infrastructure, Gemini aims to offer high accuracy, efficiency, and safety, positioning it as a competitor to models like OpenAI's GPT-4.
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Visit the following resources to learn more:
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- [@official@Google Gemini](https://gemini.google.com/)
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- [@official@Google's Gemini Documentation](https://workspace.google.com/solutions/ai/)
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- [@video@Welcome to the Gemini era](https://www.youtube.com/watch?v=_fuimO6ErKI)
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# Hugging Face Models
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Hugging Face models are a collection of pre-trained machine learning models available through the Hugging Face platform, covering a wide range of tasks like natural language processing, computer vision, and audio processing. The platform includes models for tasks such as text classification, translation, summarization, question answering, and more, with popular models like BERT, GPT, T5, and CLIP. Hugging Face provides easy-to-use tools and APIs that allow developers to access, fine-tune, and deploy these models, fostering a collaborative community where users can share, modify, and contribute models to improve AI research and application development.
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Visit the following resources to learn more:
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- [@official@Hugging Face Models](https://huggingface.co/models)
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# Inference SDK
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The Hugging Face Inference SDK is a powerful tool that allows developers to easily integrate and run inference on large language models hosted on the Hugging Face Hub. By using the `InferenceClient`, users can make API calls to various models for tasks such as text generation, image creation, and more. The SDK supports both synchronous and asynchronous operations thus compatible with existing workflows.
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Visit the following resources to learn more:
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- [@official@Inference](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client)
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- [@article@Endpoint Setup](https://www.npmjs.com/package/@huggingface/inference)
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# Inference
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In artificial intelligence (AI), inference refers to the process where a trained machine learning model makes predictions or draws conclusions from new, unseen data. Unlike training, inference involves the model applying what it has learned to make decisions without needing examples of the exact result. In essence, inference is the AI model actively functioning. For example, a self-driving car recognizing a stop sign on a road it has never encountered before demonstrates inference. The model identifies the stop sign in a new setting, using its learned knowledge to make a decision in real-time.
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Visit the following resources to learn more:
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- [@article@Inference vs Training](https://www.cloudflare.com/learning/ai/inference-vs-training/)
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- [@article@What is Machine Learning Inference?](https://hazelcast.com/glossary/machine-learning-inference/)
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- [@article@What is Machine Learning Inference? An Introduction to Inference Approaches](https://www.datacamp.com/blog/what-is-machine-learning-inference)
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# Langchain
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LangChain is a development framework that simplifies building applications powered by language models, enabling seamless integration of multiple AI models and data sources. It focuses on creating chains, or sequences, of operations where language models can interact with databases, APIs, and other models to perform complex tasks. LangChain offers tools for prompt management, data retrieval, and workflow orchestration, making it easier to develop robust, scalable applications like chatbots, automated data analysis, and multi-step reasoning systems.
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Learn more from the following resources:
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- [@official@LangChain](https://www.langchain.com/)
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- [@video@What is LangChain?](https://www.youtube.com/watch?v=1bUy-1hGZpI)
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# Limitations and Considerations
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Pre-trained models, while powerful, come with several limitations and considerations. They may carry biases present in the training data, leading to unintended or discriminatory outcomes, these models are also typically trained on general data, so they might not perform well on niche or domain-specific tasks without further fine-tuning. Another concern is the "black-box" nature of many pre-trained models, which can make their decision-making processes hard to interpret and explain.
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Visit the following resources to learn more:
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- [@article@Pre-trained Topic Models: Advantages and Limitation](https://www.kaggle.com/code/amalsalilan/pretrained-topic-models-advantages-and-limitation)
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- [@video@Should You Use Open Source Large Language Models?](https://www.youtube.com/watch?v=y9k-U9AuDeM)
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# LlamaIndex
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LlamaIndex, formerly known as GPT Index, is a tool designed to facilitate the integration of large language models (LLMs) with structured and unstructured data sources. It acts as a data framework that helps developers build retrieval-augmented generation (RAG) applications by indexing various types of data, such as documents, databases, and APIs, enabling LLMs to query and retrieve relevant information efficiently.
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Learn more from the following resources:
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- [@official@Llama Index](https://docs.llamaindex.ai/en/stable/)
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- [@video@Introduction to LlamaIndex with Python (2024)](https://www.youtube.com/watch?v=cCyYGYyCka4)
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# LLMs
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LLMs, or Large Language Models, are advanced AI models trained on vast datasets to understand and generate human-like text. They can perform a wide range of natural language processing tasks, such as text generation, translation, summarization, and question answering. Examples include GPT-5, BERT, and DeepSeek. LLMs are capable of understanding context, handling complex queries, and generating coherent responses, making them useful for applications like chatbots, content creation, and automated support. However, they require significant computational resources and may carry biases from their training data.
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Visit the following resources to learn more:
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- [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
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- [@article@Understanding AI: Everything you need to know about language models](https://leerob.com/ai)
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- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
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- [@video@Large Language Models (LLMs) - Everything You NEED To Know](https://www.youtube.com/watch?v=osKyvYJ3PRM)
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# Maximum Tokens
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The OpenAI API has different maximum token limits depending on the model being used. For instance, GPT-3 has a limit of 4,096 tokens, while GPT-4 can support larger inputs, with some versions allowing up to 8,192 tokens, and extended versions reaching up to 32,768 tokens. Tokens include both the input text and the generated output, so longer inputs mean less space for responses. Managing token limits is crucial to ensure the model can handle the entire input and still generate a complete response, especially for tasks involving lengthy documents or multi-turn conversations.
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Visit the following resources to learn more:
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- [@official@Maximum Tokens](https://platform.openai.com/docs/guides/rate-limits)
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- [@article@Guide to Free vs Paid ChatGPT Token Limits](https://tactiq.io/learn/free-vs-paid-chatgpt-token-limits-guide)
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# Mistral AI
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Mistral AI is a company focused on developing open-weight, large language models (LLMs) to provide high-performance AI solutions. Mistral aims to create models that are both efficient and versatile, making them suitable for a wide range of natural language processing tasks, including text generation, translation, and summarization. By releasing open-weight models, Mistral promotes transparency and accessibility, allowing developers to customize and deploy AI solutions more flexibly compared to proprietary models.
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Visit the following resources to learn more:
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- [@official@Mistral AI](https://mistral.ai/)
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- [@video@Mistral AI: The Gen AI Start-up you did not know existed](https://www.youtube.com/watch?v=vzrRGd18tAg)
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# Ollama Models
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Ollama provides a collection of large language models (LLMs) designed to run locally on personal devices, enabling privacy-focused and efficient AI applications without relying on cloud services. These models can perform tasks like text generation, translation, summarization, and question answering, similar to popular models like GPT. Ollama emphasizes ease of use, offering models that are optimized for lower resource consumption, making it possible to deploy AI capabilities directly on laptops or edge devices.
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Visit the following resources to learn more:
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- [@official@Ollama Model Library](https://ollama.com/library)
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- [@video@What are the different types of models? Ollama Course](https://www.youtube.com/watch?v=f4tXwCNP1Ac)
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# Ollama SDK
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The Ollama SDK is a community-driven tool that allows developers to integrate and run large language models (LLMs) locally through a simple API. Enabling users to easily import the Ollama provider and create customized instances for various models, such as Llama 2 and Mistral. The SDK supports functionalities like `text generation` and `embeddings`, making it versatile for applications ranging from `chatbots` to `content generation`. Also Ollama SDK enhances privacy and control over data while offering seamless integration with existing workflows.
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Visit the following resources to learn more:
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- [@article@SDK Provider](https://sdk.vercel.ai/providers/community-providers/ollama)
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- [@article@Beginner's Guide](https://dev.to/jayantaadhikary/using-the-ollama-api-to-run-llms-and-generate-responses-locally-18b7)
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- [@article@Setup](https://klu.ai/glossary/ollama)
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# OpenAI Assistant API
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The OpenAI Assistant API enables developers to create advanced conversational systems using models like GPT-4. It supports multi-turn conversations, allowing the AI to maintain context across exchanges, which is ideal for chatbots, virtual assistants, and interactive applications. Developers can customize interactions by defining roles, such as system, user, and assistant, to guide the assistant's behavior. With features like temperature control, token limits, and stop sequences, the API offers flexibility to ensure responses are relevant, safe, and tailored to specific use cases.
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Learn more from the following resources:
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- [@official@Assistants API](https://platform.openai.com/docs/assistants/overview)
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- [@course@OpenAI Assistants API – Course for Beginners](https://www.youtube.com/watch?v=qHPonmSX4Ms)
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# OpenAI Embedding Models
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OpenAI's embedding models convert text into dense vector representations that capture semantic meaning, allowing for efficient similarity searches, clustering, and recommendations. These models are commonly used for tasks like semantic search, where similar phrases are mapped to nearby points in a vector space, and for building recommendation systems by comparing embeddings to find related content. OpenAI's embedding models offer versatility, supporting a range of applications from document retrieval to content classification, and can be easily integrated through the OpenAI API for scalable and efficient deployment.
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Visit the following resources to learn more:
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- [@official@OpenAI Embedding Models](https://platform.openai.com/docs/guides/embeddings/embedding-models)
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- [@video@OpenAI Embeddings Explained in 5 Minutes](https://www.youtube.com/watch?v=8kJStTRuMcs)
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# OpenAI Models
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OpenAI provides a variety of models designed for diverse tasks. GPT models like GPT-5 and GPT-4 handle text generation, conversation, and translation, offering context-aware responses, while Codex specializes in generating and debugging code across multiple languages. DALL-E creates images from text descriptions, supporting applications in design and content creation, and Whisper is a speech recognition model that converts spoken language to text for transcription and voice-to-text tasks.
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Visit the following resources to learn more:
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- [@official@OpenAI Models Overview](https://platform.openai.com/docs/models)
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- [@video@OpenAI’s new “deep-thinking” o1 model crushes coding benchmarks](https://www.youtube.com/watch?v=6xlPJiNpCVw)
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# OpenAI Playground
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The OpenAI Playground is an interactive web interface that allows users to experiment with OpenAI's language models, such as GPT-3 and GPT-4, without needing to write code. It provides a user-friendly environment where you can input prompts, adjust parameters like temperature and token limits, and see how the models generate responses in real-time. The Playground helps users test different use cases, from text generation to question answering, and refine prompts for better outputs. It's a valuable tool for exploring the capabilities of OpenAI models, prototyping ideas, and understanding how the models behave before integrating them into applications.
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Visit the following resources to learn more:
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- [@official@OpenAI Playground](https://platform.openai.com/playground/chat)
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- [@video@How to Use OpenAi Playground Like a Pro](https://www.youtube.com/watch?v=PLxpvtODiqs)
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# OpenAI Assistant API
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|
||||
The OpenAI Assistant API enables developers to create advanced conversational systems using models like GPT-4. It supports multi-turn conversations, allowing the AI to maintain context across exchanges, which is ideal for chatbots, virtual assistants, and interactive applications. Developers can customize interactions by defining roles, such as system, user, and assistant, to guide the assistant's behavior. With features like temperature control, token limits, and stop sequences, the API offers flexibility to ensure responses are relevant, safe, and tailored to specific use cases.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@course@OpenAI Assistants API – Course for Beginners](https://www.youtube.com/watch?v=qHPonmSX4Ms)
|
||||
- [@official@Assistants API](https://platform.openai.com/docs/assistants/overview)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Open-Source Embeddings
|
||||
|
||||
Open-source embeddings are pre-trained vector representations of data, usually text, that are freely available for use and modification. These embeddings capture semantic meanings, making them useful for tasks like semantic search, text classification, and clustering. Examples include Word2Vec, GloVe, and FastText, which represent words as vectors based on their context in large corpora, and more advanced models like Sentence-BERT and CLIP that provide embeddings for sentences and images. Open-source embeddings allow developers to leverage pre-trained models without starting from scratch, enabling faster development and experimentation in natural language processing and other AI applications.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Embeddings](https://platform.openai.com/docs/guides/embeddings)
|
||||
- [@article@A Guide to Open-Source Embedding Models](https://www.bentoml.com/blog/a-guide-to-open-source-embedding-models)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Open vs Closed Source Models
|
||||
|
||||
Open-source models are freely available for customization and collaboration, promoting transparency and flexibility, while closed-source models are proprietary, offering ease of use but limiting modification and transparency.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@OpenAI vs. Open Source LLM](https://ubiops.com/openai-vs-open-source-llm/)
|
||||
- [@video@Open-Source vs Closed-Source LLMs](https://www.youtube.com/watch?v=710PDpuLwOc)
|
||||
@@ -1,7 +0,0 @@
|
||||
# OpenAI API
|
||||
|
||||
The OpenAI API provides access to powerful AI models like GPT, Codex, DALL-E, and Whisper, enabling developers to integrate capabilities such as text generation, code assistance, image creation, and speech recognition into their applications via a simple, scalable interface.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@OpenAI API](https://openai.com/api/)
|
||||
@@ -1,8 +0,0 @@
|
||||
# OpenAI Assistant API
|
||||
|
||||
The OpenAI Assistant API enables developers to create advanced conversational systems using models like GPT-4. It supports multi-turn conversations, allowing the AI to maintain context across exchanges, which is ideal for chatbots, virtual assistants, and interactive applications. Developers can customize interactions by defining roles, such as system, user, and assistant, to guide the assistant's behavior. With features like temperature control, token limits, and stop sequences, the API offers flexibility to ensure responses are relevant, safe, and tailored to specific use cases.
|
||||
|
||||
Learn more from the following resources:
|
||||
|
||||
- [@official@Assistants API](https://platform.openai.com/docs/assistants/overview)
|
||||
- [@course@OpenAI Assistants API – Course for Beginners](https://www.youtube.com/watch?v=qHPonmSX4Ms)
|
||||
@@ -1,8 +0,0 @@
|
||||
# OpenAI Functions / Tools
|
||||
|
||||
OpenAI Functions, also known as tools, enable developers to extend the capabilities of language models by integrating external APIs and functionalities, allowing the models to perform specific actions, fetch real-time data, or interact with other software systems. This feature enhances the model's utility by bridging it with services like web searches, databases, and custom business applications, enabling more dynamic and task-oriented responses.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Function Calling](https://platform.openai.com/docs/guides/function-calling)
|
||||
- [@video@How does OpenAI Function Calling work?](https://www.youtube.com/watch?v=Qor2VZoBib0)
|
||||
@@ -1,7 +0,0 @@
|
||||
# OpenAI Models
|
||||
|
||||
OpenAI provides a variety of models designed for diverse tasks. GPT models like GPT-3 and GPT-4 handle text generation, conversation, and translation, offering context-aware responses, while Codex specializes in generating and debugging code across multiple languages. DALL-E creates images from text descriptions, supporting applications in design and content creation, and Whisper is a speech recognition model that converts spoken language to text for transcription and voice-to-text tasks.
|
||||
|
||||
Learn more from the following resources:
|
||||
|
||||
- [@official@OpenAI Models Overview](https://platform.openai.com/docs/models)
|
||||
@@ -1,8 +0,0 @@
|
||||
# OpenAI Moderation API
|
||||
|
||||
The OpenAI Moderation API helps detect and filter harmful content by analyzing text for issues like hate speech, violence, self-harm, and adult content. It uses machine learning models to identify inappropriate or unsafe language, allowing developers to create safer online environments and maintain community guidelines. The API is designed to be integrated into applications, websites, and platforms, providing real-time content moderation to reduce the spread of harmful or offensive material.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Moderation](https://platform.openai.com/docs/guides/moderation)
|
||||
- [@article@How to use the moderation API](https://cookbook.openai.com/examples/how_to_use_moderation)
|
||||
@@ -1,8 +0,0 @@
|
||||
# OpenSource AI
|
||||
|
||||
Open-source AI refers to AI models, tools, and frameworks that are freely available for anyone to use, modify, and distribute. Examples include TensorFlow, PyTorch, and models like BERT and Stable Diffusion. Open-source AI fosters transparency, collaboration, and innovation by allowing developers to inspect code, adapt models for specific needs, and contribute improvements. This approach accelerates the development of AI technologies, enabling faster experimentation and reducing dependency on proprietary solutions.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Open Source AI Is the Path Forward](https://about.fb.com/news/2024/07/open-source-ai-is-the-path-forward/)
|
||||
- [@video@Should You Use Open Source Large Language Models?](https://www.youtube.com/watch?v=y9k-U9AuDeM)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Popular Open Source Models
|
||||
|
||||
Open-source large language models (LLMs) are models whose source code and architecture are publicly available for use, modification, and distribution. They are built using machine learning algorithms that process and generate human-like text, and being open-source, they promote transparency, innovation, and community collaboration in their development and application.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@The Best Large Language Models (LLMs) in 2024](https://zapier.com/blog/best-llm/)
|
||||
- [@article@8 Top Open-Source LLMs for 2024 and Their Uses](https://www.datacamp.com/blog/top-open-source-llms)
|
||||
@@ -1,7 +0,0 @@
|
||||
# Pricing Considerations
|
||||
|
||||
The pricing for the OpenAI Embedding API is based on the number of tokens processed and the specific embedding model used. Costs are determined by the total tokens needed to generate embeddings, so longer texts will result in higher charges. To manage costs, developers can optimize by shortening inputs or batching requests. Additionally, selecting the right embedding model for your performance and budget requirements, along with monitoring token usage, can help control expenses.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@OpenAI Pricing](https://openai.com/api/pricing/)
|
||||
@@ -1,5 +0,0 @@
|
||||
# Pricing Considerations
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@OpenAI API Pricing](https://openai.com/api/pricing/)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Prompt Engineering
|
||||
|
||||
Prompt engineering is the process of crafting effective inputs (prompts) to guide AI models, like GPT, to generate desired outputs. It involves strategically designing prompts to optimize the model’s performance by providing clear instructions, context, and examples. Effective prompt engineering can improve the quality, relevance, and accuracy of responses, making it essential for applications like chatbots, content generation, and automated support. By refining prompts, developers can better control the model’s behavior, reduce ambiguity, and achieve more consistent results, enhancing the overall effectiveness of AI-driven systems.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
|
||||
- [@video@What is Prompt Engineering?](https://www.youtube.com/watch?v=nf1e-55KKbg)
|
||||
@@ -1,8 +0,0 @@
|
||||
# RAG & Implementation
|
||||
|
||||
Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to produce more accurate, context-aware responses. It uses two components: a retriever, which searches a database to find relevant information, and a generator, which crafts a response based on the retrieved data. Implementing RAG involves using a retrieval model (e.g., embeddings and vector search) alongside a generative language model (like GPT). The process starts by converting a query into embeddings, retrieving relevant documents from a vector database, and feeding them to the language model, which then generates a coherent, informed response. This approach grounds outputs in real-world data, resulting in more reliable and detailed answers.
|
||||
|
||||
Learn more from the following resources:
|
||||
|
||||
- [@article@What is RAG?](https://aws.amazon.com/what-is/retrieval-augmented-generation/)
|
||||
- [@video@What is Retrieval-Augmented Generation? IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)
|
||||
@@ -1,9 +0,0 @@
|
||||
# RAG
|
||||
|
||||
Retrieval-Augmented Generation (RAG) is an AI approach that combines information retrieval with language generation to create more accurate, contextually relevant outputs. It works by first retrieving relevant data from a knowledge base or external source, then using a language model to generate a response based on that information. This method enhances the accuracy of generative models by grounding their outputs in real-world data, making RAG ideal for tasks like question answering, summarization, and chatbots that require reliable, up-to-date information.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is Retrieval Augmented Generation (RAG)? - Datacamp](https://www.datacamp.com/blog/what-is-retrieval-augmented-generation-rag)
|
||||
- [@article@What is Retrieval-Augmented Generation? - Google](https://cloud.google.com/use-cases/retrieval-augmented-generation)
|
||||
- [@video@What is Retrieval-Augmented Generation? - IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Replicate
|
||||
|
||||
Replicate is a platform that allows developers to run machine learning models in the cloud without needing to manage infrastructure. It provides a simple API for deploying and scaling models, making it easy to integrate AI capabilities like image generation, text processing, and more into applications. Users can select from a library of pre-trained models or deploy their own, with the platform handling tasks like scaling, monitoring, and versioning.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Replicate](https://replicate.com/)
|
||||
- [@video@Replicate.com Beginners Tutorial](https://www.youtube.com/watch?v=y0_GE5ErqY8)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Token Counting
|
||||
|
||||
Token counting refers to tracking the number of tokens processed during interactions with language models, including both input and output text. Tokens are units of text that can be as short as a single character or as long as a word, and models like GPT process text by splitting it into these tokens. Knowing how many tokens are used is crucial because the API has token limits (e.g., 4,096 for GPT-3 and up to 32,768 for some versions of GPT-4), and costs are typically calculated based on the total number of tokens processed.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@OpenAI Tokenizer Tool](https://platform.openai.com/tokenizer)
|
||||
- [@article@How to count tokens with Tiktoken](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Vector Databases
|
||||
|
||||
Vector databases are specialized systems designed to store, index, and retrieve high-dimensional vectors, often used as embeddings that represent data like text, images, or audio. Unlike traditional databases that handle structured data, vector databases excel at managing unstructured data by enabling fast similarity searches, where vectors are compared to find those that are most similar to a query. This makes them essential for tasks like semantic search, recommendation systems, and content discovery, where understanding relationships between items is crucial. Vector databases use indexing techniques such as approximate nearest neighbor (ANN) search to efficiently handle large datasets, ensuring quick and accurate retrieval even at scale.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Vector Databases](https://developers.cloudflare.com/vectorize/reference/what-is-a-vector-database/)
|
||||
- [@article@What are Vector Databases?](https://www.mongodb.com/resources/basics/databases/vector-databases)
|
||||
@@ -1,9 +0,0 @@
|
||||
# Vertex AI
|
||||
|
||||
Vertex AI is Google Cloud's fully-managed, unified development platform for building, training, deploying, and managing machine learning (ML) models at scale. It provides tools for the entire ML lifecycle, from data preparation and custom training with AutoML to model monitoring and deployment. Vertex AI offers access to Google's foundation models, such as Gemini, along with custom training options and tools for building AI agents. It streamlines the ML workflow into a single interface, supporting both low-code and custom development on managed infrastructure
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Vertex AI](https://cloud.google.com/generative-ai-studio?hl=en)
|
||||
- [@article@Vertex AI Tutorial: A Comprehensive Guide For Beginners](https://www.datacamp.com/tutorial/vertex-ai-tutorial)
|
||||
- [@video@Introduction to Vertex AI Studio](https://www.youtube.com/watch?v=KWarqNq195M)
|
||||
@@ -1,9 +0,0 @@
|
||||
# Writing Prompts
|
||||
|
||||
Prompts for the OpenAI API are carefully crafted inputs designed to guide the language model in generating specific, high-quality content. These prompts can be used to direct the model to create stories, articles, dialogue, or even detailed responses on particular topics. Effective prompts set clear expectations by providing context, specifying the format, or including examples, such as "Write a short sci-fi story about a future where humans can communicate with animals," or "Generate a detailed summary of the key benefits of using renewable energy." Well-designed prompts help ensure that the API produces coherent, relevant, and creative outputs, making it easier to achieve desired results across various applications.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
|
||||
- [@article@How to Write AI prompts](https://www.descript.com/blog/article/how-to-write-ai-prompts)
|
||||
- [@article@Prompt Engineering Guide](https://www.promptingguide.ai/)
|
||||
Reference in New Issue
Block a user