Model Context Protocol

Chain of Thought (CoT) is a prompting technique that encourages an LLM to break down complex reasoning into intermediate steps before arriving at a final answer. By explicitly reasoning through each step, models achieve significantly better accuracy on math, logic, and multi-step problems. Extended thinking and "thinking" tokens in models like Claude represent a built-in form of chain-of-thought reasoning.

Generative AI is a category of artificial intelligence that creates new content — text, images, code, music, or video — rather than just analyzing or classifying existing data. Powered by architectures like transformers and diffusion models, generative AI has transformed software development with tools like GitHub Copilot, Claude, and Cursor. It represents a shift from AI as a classification tool to AI as a creative collaborator.

Orchestration is the automated coordination of multiple services, tasks, or systems to execute a complex workflow. An orchestrator acts as a central controller that manages the sequence, parallelism, error handling, and retries of individual steps. In the context of AI agents, orchestration involves chaining LLM calls, tool use, and decision-making steps; in DevOps, it coordinates container deployment, scaling, and service discovery.

Fine-tuning is the process of further training a pre-trained AI model on a smaller, domain-specific dataset to adapt it for a particular task. Instead of training from scratch, fine-tuning adjusts existing model weights, which is significantly cheaper and faster. Common approaches include full fine-tuning, LoRA (Low-Rank Adaptation), and instruction tuning for aligning model behavior with specific requirements.

A vector database is a specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings. They enable fast similarity search, which is critical for RAG systems, recommendation engines, and semantic search applications. Popular vector databases include Pinecone, Weaviate, Qdrant, and pgvector for PostgreSQL.

Reinforcement learning (RL) is a machine learning paradigm where an agent learns optimal behavior by interacting with an environment and receiving rewards or penalties. RLHF (Reinforcement Learning from Human Feedback) is a key technique used to align LLMs with human preferences, making their outputs more helpful and safe. RL is also behind breakthroughs in game-playing AI and robotics.