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Enterprise Natural Language Processing with LLM: Advanced Architectures for Production-Ready AI in Python
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Publisher: Independently Published
Pages: 146
Weight: 0.58
Height: 0.31 Width: 7.00 Depth: 10.00
Language: English
Inside, you'll learn how to:
- Architect LLM-powered applications, including agentic and conversational systems
- Design advanced RAG architectures with hybrid search, re-ranking, query transformation, and graph-based context
- Manage prompts, context windows, token limits, versioning, and complex workflows
- Build and orchestrate multi-agent systems in Python with routing, human-in-the-loop validation, and execution tracing
- Control LLM costs through semantic caching, model routing, batching, and API optimization
- Fine-tune and self-host open-source models using LoRA, QLoRA, vLLM, TGI, and TensorRT-LLM
- Defend AI applications against prompt injection, jailbreaks, data leakage, and other security risks
- Establish LLMOps pipelines for evaluation, A/B testing, model management, and continuous deployment
- Design model-agnostic architectures capable of adapting to evolving AI technologies
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