WelcomeOverviewPrompt Engineering TechniquesRAG vs. fine-tuning vs. prompt engineeringOverviewTutorial: Prompt Chaining with LangchainTree of Thoughts (ToT)Meta promptingIterative promptingReAct PromptingOverviewOne Shot PromptingFew Shot PromptingOverviewPrompt 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 modelJobit Varugheseartificial intelligencedatasetsfine-tuningmachine learningsupervised learninglarge language modelcontext windowdeep learningfew-shot learningprompt engineeringsmaller modelsZero-shot promptingOne-shot promptingFew-shot promptingChain-of-thought promptingbenchmarksprompt engineeringmodel parametersgradient descenttransformer modelslinear regressionzero-shotnatural language processingEbook
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Prompt engineering with watsonx.ai
Gain a comprehensive understanding of prompt engineering, learn techniques to achieve the best results with LLMs, and apply learnings through completion of a diverse set of prompt engineering exercises.
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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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IBM watsonx Developer Hub
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Prompt engineering fundamentals
Go from zero to hero with prompt templates for different types of prompts.
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