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The Complete LLM & Generative AI Tech Stack Foundation Models & Core Technologies ├── Large Language Models │ ├── OpenAI: GPT-4, GPT-4o │ ├── Anthropic: Claude 3 │ ├── Meta: Llama 2, Llama 3 │ └── Google: Gemini, PaLM 2 ├── Open Source Alternatives │ ├── Mistral AI models │ ├── Falcon, Vicuna │ ├── CodeLlama, StarCoder │ └── Custom fine-tuned models └── Model Architectures ├── Transformer-based ├── Attention mechanisms ├── Encoder-decoder models └── Auto-regressive models Development Frameworks & SDKs ├── Python Libraries │ ├── LangChain, LlamaIndex │ ├── Transformers │ ├── PyTorch, TensorFlow │ └── JAX, Flax ├── API Integration │ ├── OpenAI Python SDK │ ├── Anthropic API │ ├── Google AI Python SDK │ └── REST API wrappers └── Development Tools ├── Jupyter Notebooks ├── VS Code with AI extensions ├── Google Colab, Kaggle └── Weights & Biases Prompt Engineering & Optimization ├── Prompt Design Patterns │ ├── Zero-shot, Few-shot learning │ ├── Chain-of-Thought │ ├── ReAct │ └── Self-consistency, Tree of Thoughts ├── Prompt Management │ ├── Prompt templates │ ├── Version control for prompts │ ├── A/B testing frameworks │ └── Prompt optimization tools └── Advanced Techniques ├── Function calling ├── Tool use & planning ├── Memory management └── Multi-modal prompting RAG & Knowledge Enhancement ├── Retrieval-Augmented Generation │ ├── Vector databases │ ├── Document loaders & parsers │ ├── Embedding models │ └── Hybrid search systems ├── Vector Databases │ ├── Pinecone, Weaviate │ ├── Chroma, Qdrant │ ├── Milvus, Vespa │ └── PostgreSQL with pgvector └── Data Processing ├── Text chunking strategies ├── Embedding generation ├── Semantic search └── Knowledge graph integration Application Patterns & Architectures ├── Common Patterns │ ├── AI Agents │ ├── Conversational AI │ ├── Content generation systems │ └── Code generation ├── System Design │ ├── Chat interfaces │ ├── Streaming responses │ ├── Caching strategies │ └── Rate limiting └── Integration Patterns ├── API gateways for AI services ├── Middleware for LLM call └── Circuit breakers Model Management & Deployment ├── Model Serving │ ├── Hugging Face Inference Endpoints │ ├── AWS SageMaker, Google Vertex AI │ ├── Azure Machine Learning │ └── Custom model servers ├── Fine-tuning │ ├── LoRA, │ ├── Parameter-efficient fine-tuning │ ├── RLHF │ └── Custom training pipelines └── Optimization ├── Quantization ├── Pruning ├── Model compression └── Hardware acceleration Monitoring & Evaluation ├── Performance Metrics │ ├── Latency │ ├── Cost per request tracking │ ├── Token usage analytics │ └── Quality metrics ├── LLM-specific Monitoring └── Evaluation Frameworks Security & Responsible AI ├── Safety Measures │ ├── Content moderation │ └── Ethical guidelines ├── Privacy │ ├── Data anonymization │ ├── GDPR │ └── Audit trails └── Risk Management ├── Bias detection ├── Fairness metrics ├── Transparency reports └── Response plans Emerging Technologies & Future Trends ├── Multi-modal AI │ ├── Vision-Language models │ ├── Audio processing │ ├── Video understanding │ └── Cross-modal retrieval ├── Advanced Architectures │ ├─MoE │ ├── Retrieval-based models │ ├── Neuro-symbolic AI │ └── World models └── Production Considerations Get my Complete LLM and Generative AI Development eBook here: codewithdhanian.gumroad.com/…