فهرست مطالب

• Preface
• Chapter 1: Getting Started with Generative AI
• Technical requirements
• Defining the terminology
• The generative AI stack
• Important features of generative AI
• Summary
• Chapter 2: Building Blocks of Intelligent Applications
• Technical requirements
• Defining intelligent applications
• LLMs – reasoning engines for intelligent apps
• Embedding models and vector databases – semantic long-term memory
• Your (soon-to-be) intelligent app
• Summary
• Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
• Chapter 3: Large Language Models
• Technical requirements
• Probabilistic framework
• Machine learning for language modelling
• ANNs for natural language processing
• Dealing with sequential data
• LLMs in practice
• Summary
• Chapter 4: Embedding Models
• Technical requirements
• What is an embedding model?
• Choosing embedding models
• Best practices
• Summary
• Chapter 5: Vector Databases
• Technical requirements
• What is a vector embedding?
• Graph connectivity
• The need for vector databases
• Case studies and real-world applications
• Vector search best practices
• Summary
• Chapter 6: AI/ML Application Design
• Technical requirements
• Data modeling
• Data storage
• Data flow
• Freshness and retention
• Security and RBAC
• Best practices for AI/ML application design
• Summary
• Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
• Chapter 7: Useful Frameworks, Libraries, and APIs
• Technical requirements
• Python for AI/ML
• AI/ML frameworks
• Key Python libraries
• AI/ML APIs
• Summary
• Chapter 8: Implementing Vector Search in AI Applications
• Technical requirements
• Information retrieval with MongoDB Atlas Vector Search
• Building RAG architecture systems
• Summary
• Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
• Chapter 9: LLM Output Evaluation
• Technical requirements
• What is LLM evaluation?
• Model benchmarking
• Evaluation metrics
• Summary
• Chapter 10: Refining the Semantic Data Model to Improve Accuracy
• Technical requirements
• Embeddings
• Embedding metadata
• Optimizing retrieval-augmented generation
• Summary
• Chapter 11: Common Failures of Generative AI
• Technical requirements
• Hallucinations
• Sycophancy
• Data leakage
• Cost
• Performance issues in generative AI applications
• Summary
• Chapter 12: Correcting and Optimizing Your Generative AI Application
• Technical requirements
• Baselining
• Testing and red teaming
• Information post-processing
• Other remedies
• Summary
• Appendix: Further Reading
• Index
• Other Books You May Enjoy