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Prompt Engineer

A comprehensive guide for Prompt Engineer

""1FOUNDATIONS OF ARTIFICIAL INTELLIGENCE AND LARGE LANGUAGE MODELS (LLMS)2CORE PROMPT ENGINEERING PRINCIPLES3ADVANCED PROMPTING STRATEGIES4DOMAIN-SPECIFIC PROMPT ENGINEERING5WORKING WITH APIS, TOOLING, AND DEPLOYMENT6ETHICS, SECURITY, AND GOVERNANCEPrompt EngineerIntroduction to AI and Machine LearningHistory and Evolutionof AISupervised,Unsupervised, andReinforcement LearningDeep Learning BasicsUnderstanding Natural Language Processing (NLP)Tokenization and TextRepresentationEmbeddings and VectorSpacesThe TransformerArchitectureAttention MechanismsMechanics of Large Language ModelsPre-training vs.Fine-tuningAutoregressiveGenerationModel Sizes andParametersScaling LawsLimitations,Hallucinations, andStochastic ParrotsMultimodal LLMsVisionAudioVideoThe Anatomy of a PromptInstructions, Context,Input Data, and OutputIndicatorsSystem, User, andAssistant RolesClarity, Specificity,and FormattingBasic Prompting TechniquesZero-Shot PromptingFew-Shot PromptingRole-Playing andPersona AdoptionPrompt Templates andParameterizationMeta-PromptingIterative RefinementAnalyzing ModelResponsesDebugging PromptsA/B Testing PromptsTemperature, Top-P,and Top-K TweakingReasoning and LogicChain of Thought (CoT)Zero-Shot and Few-ShotSelf-ConsistencyTree of Thoughts (ToT)and Graph of ThoughtsGoTStep-Back PromptingDirectional StimulusPromptingAgentic and Interactive FrameworksReAct (Reasoning andActing) FrameworkSelf-Reflection andSelf-CorrectionPlan-and-SolvePromptingMulti-Agent Debatesand CollaborationTool Use and Function CallingUnderstanding FunctionCallingDesigning Tool and APIDescriptionsJSON Schema forStructured OutputsError Handling andFallbacksContext and Information ManagementRetrieval-AugmentedGeneration (RAG)BasicsAdvanced RAGGraphRAGSelf-RAGCRAGChunking Strategiesand Semantic SearchHandling Long ContextWindows andNeedle-in-a-HaystackPrompt Chaining andState ManagementConstraining and Formatting OutputsEnforcing StructuredOutputJSONXMLYAMLLength, Tone, andStyle ConstraintsNegative Prompting andAvoidanceAutomated Prompt EngineeringAutomatic PromptEngineerAPEDSPy and DeclarativePromptingOPROOptimization byPromptingPrompt OptimizationAlgorithmsCoding and Software DevelopmentCode Generation,Completion, andExplanationCode Review,Refactoring, and TestGenerationUsing AI Native IDEsCursorGitHub CopilotContent Creation and CopywritingSEO Optimization andBrand Voice MatchingCreative Writing,Brainstorming, andIdeationPersona-DrivenCopywritingData Analysis and ExtractionEntity Extraction fromUnstructured TextSummarizationTechniquesTranslating NaturalLanguage to SQL/CodeNL2SQLSynthetic Data GenerationGeneratingHigh-Quality DatasetsData Augmentation forFine-TuningDistillation and ModelTuning PipelinesMultimodal PromptingText-to-ImageGenerationMidjourneyDALL-EStable DiffusionText-to-Video andAudio PromptsVisual QuestionAnsweringVQALLM APIs and InferenceOpenAI, Anthropic,Google Gemini APIsAPI ParametersTemperatureFrequency PenaltyPresence PenaltyOpen Weights Modelsvia Hugging Face andOllamaModel Routing,Fallbacks, and RateLimitingOrchestration FrameworksLangChain andLangGraphLlamaIndexSemantic Kernel andAutoGenProduction and Observability (LLMOps)Semantic CachingPrompt Tracing andObservabilityLangSmithHeliconePhoenixPrompt Versioning andContent ManagementSystemsCMSPrompt EvaluationQuantitative vs.Qualitative EvaluationLLM-as-a-JudgeEvaluation FrameworksRAGASBLEUROUGEBERTScoreGolden Datasets andGround TruthMetricsPerplexityExact MatchSemantic SimilarityPrompt Injection and SecurityUnderstanding PromptInjection AttacksJailbreakingTechniques andDefensesDefensive PromptingStrategiesData PoisoningAwarenessBias, Fairness, and AlignmentIdentifying Model BiasMitigating Bias viaPromptsRLHF (ReinforcementLearning from HumanFeedback) and RLAIFCompliance, Privacy, and Red TeamingData Privacy andPII/PHI MaskingCopyright and LegalConsiderationsRed Teaming LLMApplications