Fine-tuning

In AI, hallucination refers to when a language model generates confident-sounding but factually incorrect or fabricated information. This occurs because LLMs predict statistically likely text rather than retrieving verified facts. Mitigation strategies include RAG, grounding responses in source documents, structured output validation, and using temperature settings to reduce creative deviation.

A large language model (LLM) is a deep learning model trained on massive text datasets to understand and generate human-like text. LLMs like GPT, Claude, and LLaMA power chatbots, code assistants, and content generation tools. They work by predicting the next token in a sequence based on learned statistical patterns across billions of parameters.

ETL (Extract, Transform, Load) is an automated data processing pattern where data is extracted from source systems, transformed into a desired format or structure, and loaded into a target system like a data warehouse. Modern variations include ELT, where raw data is loaded first and transformed in place. ETL pipelines are essential for automating data integration, reporting, and feeding clean data into ML training workflows.

A neural network is a computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process data by adjusting weighted connections during training. Deep neural networks with many layers form the foundation of modern AI, powering everything from image recognition to language understanding. Common architectures include feedforward networks, convolutional networks (CNNs), and transformers.

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.

Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI applications connect to external data sources and tools. MCP provides a universal interface for LLMs to access databases, APIs, file systems, and other services through standardized server implementations. It enables building AI applications that can interact with the real world in a structured, secure way.