Topic: Prompt engineering
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Prompting Generative AI Chat Models: A Comprehensive Guide
Explore the world of generative AI chat models with our comprehensive guide. Learn how to effectively prompt these tools for applications in content creation, education, and more, while understanding their unique capabilities and limitations.
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Context Engineering: The Future of Coding
Context engineering is a systematic approach to structuring comprehensive inputs for LLMs, surpassing traditional prompt engineering by integrating documents, data, and tools for complex tasks. Industry leaders highlight context engineering as developer-centric, requiring pipelines that pull from...
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RAG: The Essential AI Tool Marketers Need to Know
Retrieval-Augmented Generation (RAG) enhances AI outputs by integrating targeted external data, addressing issues like hallucinations and generic responses in marketing applications. RAG's success depends on high-quality, structured data, including machine-readable inputs and precise retrievabili...
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23 Must-Know AI Terms: Your Essential ChatGPT Glossary
autonomous agents: An AI model that have the capabilities, programming and other tools to accomplish a specific task. large language model, or LLM: An AI model trained on mass amounts of text data to understand language and generate novel content in human-like language. multimodal AI: A type of AI that can process multiple types of inputs, including text, images, videos and speech. tokens: Small bits of written text that AI language models process to formulate their responses to your prompts. we...
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Prompt Ops: How to Cut Hidden AI Costs from Poor Inputs
Optimizing AI inputs reduces costs by minimizing computational expenses tied to token processing, as inefficient prompts lead to higher energy use and operational overhead. Clear, structured prompts improve efficiency by guiding models to concise outputs and avoiding unnecessary verbosity...
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