@aibuilder

Model Context Protocol - An open standard developed by Anthropic for connecting AI assistants to external tools, data sources, and services. Enables AI agents to interact with the world in standardized ways.

“Our platform exposes all its APIs via MCP so any AI assistant can integrate with it.”

The craft of designing, structuring, and refining inputs (prompts) to elicit desired outputs from large language models. A skilled prompt engineer understands how to use context, examples, formatting, and instruction clarity to guide model behavior without changing the underlying weights.

“Good prompt engineering turned an unreliable prototype into a production-ready feature in just a week.”

A capability that allows language models to invoke external functions, APIs, or services during generation. The model decides when to call a tool, formats the call arguments as JSON, receives the result, and incorporates it into its response — enabling real-world action beyond text generation.

“The agent used tool calling to check the current weather before generating its travel recommendations.”

A prompting technique where a language model is encouraged or required to show its step-by-step reasoning before providing a final answer. Chain-of-thought prompting significantly improves accuracy on complex tasks like math, logic puzzles, and multi-step planning.

“Adding "let's think step by step" to the prompt triggered chain-of-thought reasoning and doubled accuracy.”

A prompting approach where a small number of input-output examples are included in the context to guide model behavior on a new task. Few-shot prompting helps models understand the desired format, tone, or logic without any weight updates.

“We gave the model three few-shot examples of our data format and it immediately understood the pattern.”

A class of generative AI model that learns to create images, audio, or video by reversing a noise-adding process. During training the model learns to denoise progressively; during generation it starts from pure noise and iteratively refines it into a coherent output. Stable Diffusion and DALL·E 3 are prominent examples.

“The diffusion model generated photorealistic product photos from text descriptions in seconds.”

Describing a system architecture where multiple AI agents collaborate, delegate, or compete to accomplish a shared goal. Multi-agent systems can parallelize work, specialize roles, and check each other's outputs, enabling tasks too complex for a single agent context window.

“The multi-agent pipeline had a planner agent, a coder agent, and a reviewer agent working in sequence.”

The coordination and sequencing of multiple AI agents, services, or steps in an automated workflow. An orchestrator determines which tools to invoke, in what order, and how to pass outputs between steps to complete a complex task end-to-end.

“The orchestration layer decided to call the search tool before invoking the summarization agent.”

A style of software development where the programmer communicates intent, goals, and aesthetic in natural language to an AI coding assistant rather than writing precise code themselves. The developer "vibes" with the AI, iterating conversationally until the software feels right, without necessarily understanding every line of generated code.

“He built the entire MVP in a weekend through vibe coding, just describing what he wanted to the AI.”