ref: hugging-face
ref: hugging-face
Large Language Models (LLMs)
Neural networks trained on vast text corpora that generate text by predicting sequences of tokens, basic units of text processing that typically represent words, parts of words, or individual characters [25]. For instance, the word “unhappily” might be tokenized as “un”, “##happi”, “##ly”, where ## marks tokens that are not the start of a word.
Agent
An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional software that follows predetermined paths, agents can adapt and reason through complex scenarios.
Agentic AI
AI systems designed to act autonomously on behalf of users, making decisions and taking actions without constant human intervention. These systems can plan, execute tasks, and adapt to changing conditions.
Multi-Agent System (MAS)
A collection of multiple AI agents that work together, communicate, and coordinate to solve complex problems that would be difficult for a single agent to handle alone.
Agent Orchestration
The coordination and management of multiple agents working together, including task distribution, communication protocols, and result aggregation.
Retrieval-Augmented Generation (RAG)
A technique that combines information retrieval with text generation, allowing agents to access external knowledge sources to provide more accurate and up-to-date responses.
Model Context Protocol (MCP)
An open standard for how AI systems integrate and share data with external tools, systems, and data sources.
Token
The basic unit of text processing in AI models, which can represent words, parts of words, or characters.
Tokenization
The process of breaking down text into tokens that can be processed by AI models.
Context windows
Define the maximum number of tokens an LLM can consider when generating responses. State-of-the-art models typically handle hundreds of thousands to millions of tokens, constraining how much code and documentation they can simultaneously process. When context limits are exceeded, models lose track of earlier information. Even when information is contained within the context window, attention to mid-document details can degrade (“lost in the middle”), especially for models with very large context windows; this phenomenon is known as context rot.
In-context learning
Allows models to adapt their behavior based on examples and instructions provided within the current conversation, without permanent changes to the underlying model. This enables direction of model behavior through strategic provision of examples and formatting of instructions.
Prompting
Encompasses techniques for structuring inputs to elicit desired outputs, including clear requirement specification, strategic provision of examples, and structured formatting. Effective prompting can dramatically improve code quality and relevance.
Principle of Least Privilege
A security concept where users and systems are given the minimum levels of access needed to perform their functions.
Telemetry
The automated collection and transmission of data from remote systems for monitoring and analysis.
Prompt Injection
A security vulnerability where malicious input is crafted to manipulate an AI model's behaviour or extract sensitive information.
Hallucination
When an AI model generates information that appears plausible but is factually incorrect or not based on its training data.
ref: wikipedia, agentic-ai-terminology github, arxiv:2510.22254v2-"Ten Simple Rules for AI-Assisted Coding in Science"
From M. Sparks's page
No GenAI usage - We all start here, but there can be good reasons to choose this level. Examples: learning exercises, controlled data, secure environments, formal assurance, environmental or ethical concerns, or projects that have not agreed a policy.
Conversational interaction - Conversations asking for explanations, background, design trade-offs, unfamiliar errors and conceptual help. Examples: "Explain this compiler error", "What should I test?", "What assumptions does this numerical method make?", "What misunderstandings might the average developer have based on this code?" (for sanity checking API design)
Conversational artefact creation - Chat-based creation of small, reviewable outputs. Examples: a helper function, a test skeleton, a README paragraph, a regular expression, or a command-line usage example.
Conversational Creation with Zip Files (etc) - Providing selected files, snippets, logs or zip files so the tool can reason about the content. Examples: "Suggest tests for these files", "Review this small package structure", "Explain why this minimal example fails."
IDE autocomplete and inline suggestions - AI suggestions appear while editing. Examples: completing boilerplate, filling repetitive test cases, suggesting docstrings or small local code patterns.
Editor-integrated local assistant - The assistant can make bounded edits inside the editor. Examples: refactor a function, add type hints, generate tests for a module, update these docstrings.
Repository-aware assistance - The tool can search and reason across the repository. Examples: trace where a setting is used, identify tests for a feature, propose where to add a new option.
Constrained local tool-using agents - An agent can edit files, run commands and iterate locally under supervision. Examples: fix a failing test on a disposable checkout, run linting, prepare a patch without pushing.
Managed or cloud coding agents - A hosted or managed agent works in a configured environment, often asynchronously. Examples: ask a cloud coding agent to implement a small issue, run tests, and prepare a branch or PR for review.
CI/PR/repository agents - Agents are integrated into shared project workflows. Examples: review PRs, suggest CI fixes, triage issues, draft dependency updates, or maintain an AI staging repository.
Open-ended autonomous agents - Broad tool-using systems with persistence, scheduling or external integrations. Examples: agents that can act across repositories, services, email, cloud resources or multiple tools over time.