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The curriculum explains how to structure training pipelines, handle different data formats, implement various fine-tuning approaches (full fine-tuning, LoRA, prompt tuning), and measure model performance. 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This includes both empirical best practices and theoretical understanding of why certain prompting strategies work better than others for different model sizes and capabilities.","intents":["I want to improve my LLM application's output quality by optimizing prompts rather than retraining","I need to understand when to use few-shot vs zero-shot prompting and how to structure examples","I'm building a system that needs to handle diverse tasks and want to know how to adapt prompts dynamically","I want to understand why chain-of-thought prompting works and how to apply it to my use cases"],"best_for":["Product teams building LLM-powered applications looking for quick performance improvements","Prompt engineers and AI specialists optimizing model outputs","Developers integrating LLMs into applications without access to fine-tuning","Teams experimenting with different LLM models and needing model-agnostic techniques"],"limitations":["Prompt engineering is empirical and results vary significantly across models and tasks","Techniques may not transfer well between different model families or sizes","No systematic way to discover optimal prompts — requires experimentation and iteration","Performance gains from prompting alone are limited compared to fine-tuning for specialized tasks"],"requires":["Access to an LLM API or local model (OpenAI, Anthropic, open-source, etc.)","Understanding of the specific task and domain","Ability to evaluate model outputs qualitatively and quantitatively","Basic understanding of how LLMs process text and generate responses"],"input_types":["task descriptions and requirements","example inputs and desired outputs","domain-specific context and constraints","feedback on model outputs for iteration"],"output_types":["optimized prompt templates","improved model outputs for target tasks","prompt engineering guidelines for specific domains","evaluation metrics showing performance improvements"],"categories":["text-generation-language","planning-reasoning","education-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-11-667-large-language-models-methods-and-applications-carnegie-mellon-university__cap_4","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.system.design.and.implementation","name":"retrieval-augmented generation (rag) system design and implementation","description":"Teaches how to build RAG systems that augment LLM generation with retrieved context from external knowledge sources, covering document indexing, retrieval mechanisms, ranking strategies, and integration with generation models. 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The curriculum covers different agent architectures (ReAct, chain-of-thought, hierarchical planning), how to structure tool definitions for function calling, and strategies for handling agent failures and loops. This includes both the theoretical foundations of planning and practical implementation patterns for building reliable agents.","intents":["I want to build an autonomous agent that can break down complex tasks and use tools to solve them","I need to understand how to structure function definitions and tool descriptions for LLM function calling","I'm implementing an agent system and want to know how to handle failures, loops, and recovery","I want to design a multi-step reasoning system that can plan and execute complex workflows"],"best_for":["Teams building autonomous AI agents and workflow automation systems","Developers implementing tool-using LLM applications","Product teams creating AI assistants with complex reasoning requirements","Researchers exploring LLM-based planning and decision-making"],"limitations":["Agent reliability decreases with task complexity — multi-step reasoning is error-prone","Difficult to predict agent behavior and ensure safety in open-ended scenarios","Requires careful tool design and error handling to prevent infinite loops or failures","Computational cost scales with reasoning steps — complex agents may be expensive to run"],"requires":["Understanding of LLM capabilities and limitations","Knowledge of planning algorithms and search strategies","Ability to define tools and APIs that agents can use","Experience with prompt engineering and chain-of-thought techniques","LLM API or local model with function calling support"],"input_types":["task descriptions and goals","tool definitions and API specifications","examples of agent reasoning and planning","feedback on agent behavior for refinement"],"output_types":["agent implementations and code","planning strategies and decision trees","tool definitions and function schemas","evaluation of agent performance and reliability"],"categories":["planning-reasoning","tool-use-integration","automation-workflow","education-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-11-667-large-language-models-methods-and-applications-carnegie-mellon-university__cap_6","uri":"capability://data.processing.analysis.llm.evaluation.benchmarking.and.metrics.instruction","name":"llm evaluation, benchmarking, and metrics instruction","description":"Teaches how to evaluate LLM performance across different dimensions including accuracy, fluency, factuality, safety, and efficiency, covering both automatic metrics and human evaluation methodologies. 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