WelcomeOverviewPrompt Engineering TechniquesRAG vs. fine-tuning vs. prompt engineeringOverviewTutorial: Prompt Chaining with LangchainTree of Thoughts (ToT)Meta promptingTutorial - IT incident analysis and resolution assistantReAct PromptingOverviewOne Shot PromptingOverviewTutorial: Few Shot Prompting with LangchainOverviewPrompt Injection PreventionHow AI can be hacked with prompt injectionAI jailbreakWhen AI chatbots break badOverviewOverviewPrompt Engineering with DSPyOverviewTutorial: Implementation of prompt cachingChain of thoughtsDirectional stimulus promptingTutorial: Role promptingIn-context learningOverviewTutorial: Prompt tune a Granite modelVrunda Gadeshalarge language models (LLMs)IBM® Granite™ modelsIBM Cloud® accountwatsonx.ai™watsonx.aiwatsonx.ai projectJupyter NotebookIBM Granite™ CommunityGitHubwatsonx.ai Runtimewatsonx.aiVrunda GadeshaEbook
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Prompt engineering with watsonx.ai
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Locally differentially private document generation using zero shot prompting
See how this publication demonstrates that pretrained large language models can effectively contribute to privacy preservation.
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Adversarial prompting: Testing and strengthening the security and safety of LLMs
Explore various techniques involved in adversarial prompting, focusing on how these tactics can be used to test and strengthen the security and safety of large language models.
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Prompt engineering fundamentals
Go from zero to hero with prompt templates for different types of prompts.
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Explore developer resourcesExplore watsonx OrchestrateExplore AI development toolsExplore AI servicesExplore watsonx OrchestrateExplore AI development toolshttps://arxiv.org/abs/2406.00507https://ieeexplore.ieee.org/document/10172550https://dl.acm.org/doi/10.1145/3491101.3519729https://arxiv.org/abs/2308.04138
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