LLMs in Enterprise

Introduction to Large Language Models
    From unstructured data to LLMs
    Enterprise application of LLMs
    Challenges in scaling and deploying LLMs
    LLM design patterns
    Summary
    References
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LLMs in Enterprise: Applications, Challenges, and Design Patterns
    Technical requirements
    Training foundational models
    Approaches to integration in business processes
    Summary
    References
Advanced Fine-Tuning Techniques and Strategies for Large Language Models
    Technical requirements
    Introduction to retrieval-augmented generation
    Foundations of retrieval mechanisms
    Embeddings for enhanced retrieval
    Ensuring attribution, reducing hallucinations, and facilitating revisions
    Summary
    Reference
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Retrieval-Augmented Generation Pattern
    Technical requirements
    The importance of contextual customization
    RAGs in enterprise
    The complexity of prompt engineering
    Use case – using RAG to enhance information retrieval
    Summary
    References
Customizing Contextual LLMs
Part 2: Advanced Design Patterns and Techniques
    Technical requirements
    Foundations of prompt engineering
    The power of prompt engineering
    Understanding the science behind prompt engineering
    Leveraging structure and keywords for prompt effectiveness
    Zero-shot and few-shot learning prompts
    Managing and mitigating hallucinations in LLMs
    Summary
    References
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The Art of Prompt Engineering for Enterprise LLMs
    Technical requirements
    The role of evaluation patterns
    Response variability and robustness
    Enterprise-specific challenges in evaluating LLM applications
    Recommendations for approaching LLM evaluation
    Bridging evaluation and metrics: a data-driven approach
    Metrics for evaluating LLM performance
    Summary
    References
Enterprise Challenges in Evaluating LLM Applications
    Technical requirements
    Importance of data in LLM development
    Case studies on effective data strategies
    Data quality and preprocessing
    Data annotation and labeling
    Data partitioning
    Data standardization and normalization
    Summary
    References
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The Data Blueprint: Crafting Effective Strategies for LLM Development
    Technical requirements
    Managing latency and throughput for business needs
    Meeting stringent business and regulatory requirements
    Summary
    References
Managing Model Deployments in Production
    Technical requirements
    Introduction to advanced inferencing engines
    Deployment engines – comparative analysis
    Performance comparisons and trade-offs
    Advanced topics and emerging trends
    Summary
    References
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Accelerated and Optimized Inferencing Patterns
Part 3: GenAI in the Enterprise
    Technical requirements
    Introduction to connected LLM systems
    Architectures for connected LLMs
    Key enabling technologies
    Performance optimization
    Advanced patterns
    Summary
    References
Connected LLMs Pattern
    Technical requirements
    Strategies for continuous system monitoring
    Building reliable and robust LLM systems
    Securing LLMs – privacy, threats, and compliance
    Optimizing costs and scaling deployments
    Field insights and the future of LLM operations
    Summary
    References
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Monitoring LLMs in Production
    Technical requirements
    Defining responsible AI in LLMs
    Ethical considerations in LLMs
    Implementing transparency and accountability
    Safety and robustness in LLMs
    Summary
    References
Responsible AI in LLMs
    Technical requirements
    Emerging trends in AI
    Understanding multimodality in LLMs
    Technological advances in multimodal AI
    A multimodal use case in the medical domain
    Summary
    References
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Emerging Trends and Multimodality
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