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LLM Applications
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A production-minded guide to building applications with hosted and open-source large language models, including prompting, RAG, agents, evaluation, safety, and cost control.
Level: intermediateLanguage: EN EnglishRating: 0Reviews: 0
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01Chapter 1: Introduction to Large Language Models02Chapter 2: Transformer Architecture Explained03Chapter 3: LLM Ecosystem Overview04Chapter 4: Prompt Engineering Fundamentals05Chapter 5: Advanced Prompting Techniques06Chapter 6: Prompt Optimization and Evaluation07Chapter 7: LLM APIs and SDKs08Chapter 8: Application Architectures09Chapter 9: Frontend Integration10Chapter 10: Introduction to RAG11Chapter 11: Embeddings and Vector Databases12Chapter 12: Building a RAG Pipeline13Chapter 13: Advanced RAG Techniques14Chapter 14: Overview of Open Models15Chapter 15: Running LLMs Locally16Chapter 16: Fine-Tuning and Customization17Chapter 17: Introduction to AI Agents18Chapter 18: Tool Use and Function Calling19Chapter 19: Multi-Agent Systems20Chapter 20: Conversational AI Systems21Chapter 21: Content Generation22Chapter 22: AI in Software Development23Chapter 23: Data Analysis and BI24Chapter 24: LLM Risks and Limitations25Chapter 25: Security in LLM Applications26Chapter 26: AI Ethics and Responsible AI27Chapter 27: Cost Optimization28Chapter 28: Latency Optimization29Chapter 29: Evaluation and Monitoring30Chapter 30: Multimodal LLMs31Chapter 31: Fine-Tuned Vertical AI32Chapter 32: The Future of AI Applications33Appendix E: Deeper Practice and Extra Examples
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