🔷 LLM Usage Cheatsheet ➤ Key Benefits - Automates tasks → Saves time, boosts efficiency - Improves accuracy → Reduces errors - Scales easily → Handles large datasets - Cuts costs → Minimizes manual work - Personalized insights → Adapts to users ➤ Top Use Cases - Content creation: Blogs, ads, scripts - Code assistance: Debugging, auto-complete - Customer support: AI chatbots - Data analysis: Reports, trends - Healthcare & legal: Research, compliance ➤ Optimization Tips - Prompt engineering: Craft precise prompts - Context management: Use embeddings - Memory handling: Vector DBs for long chats - Multi-LLM strategy: Specialized LLMs per task ➤ Choosing the Right LLM - Accuracy → Domain-specific (BioGPT) - Speed → Smaller models (Mistral) - Privacy → Self-hosted (LLaMA) - Cost → Open-source (Falcon) - Scalability → Cloud APIs (GPT-4 API) ➤ Implementation Strategies - API-based - Local models - Hybrid approaches - Fine-tuning - Vector search ➤ Popular Models & Tools - GPT-4, Claude, Codex, StarCoder - Pegasus, NLLB-200, Minerva - BioGPT, Med-PaLM - LangChain, Pinecone, Weaviate, OpenRouter, LoRA, Hugging Face
