AI & Vibe Coding Glossary

Agentic AI refers to AI systems that can autonomously pursue goals, make decisions, and take actions without constant human supervision. These systems can plan, execute, evaluate results, and adjust their approach based on outcomes.

Unlike simple question-answer AI, agentic systems can break down complex tasks, use tools, gather information, and iterate until they achieve their objectives. They exhibit goal-directed behavior and can handle unexpected situations adaptively.

For AI builders, agentic AI represents the evolution from AI assistants to AI agents that can handle complete workflows. Building agentic systems requires careful consideration of safety, reliability, and monitoring to ensure they operate within intended boundaries.

AgenticOps encompasses the practices, tools, and processes for deploying, monitoring, and maintaining AI agents in production environments. It's the AI equivalent of DevOps, focusing on reliability, observability, and continuous improvement of autonomous AI systems.

Key aspects include agent monitoring, performance tracking, error handling, safety controls, and iterative improvement based on production data. It ensures AI agents operate within defined parameters while maintaining effectiveness and reliability.

For AI builders, AgenticOps is essential for scaling from experimental agents to production systems. It involves implementing logging, metrics, alerts, and controls to ensure agents perform reliably and safely in real-world conditions.

An AI agent is an autonomous system that perceives its environment, makes decisions, and takes actions to achieve specific goals. Unlike traditional AI that responds to prompts, agents can initiate actions, use tools, and work independently toward objectives.

AI agents combine language understanding with the ability to interact with external systems, APIs, and tools. They can perform multi-step tasks like research, data analysis, or process automation without step-by-step human guidance.

For AI builders, agents represent a paradigm shift from building AI-powered features to creating AI colleagues that can handle entire job functions. This requires robust error handling, monitoring, and clear boundaries to ensure reliable, safe operation.

AI hallucination occurs when an AI model generates information that sounds plausible but is actually false or nonsensical. The AI confidently presents made-up facts, incorrect dates, fictional citations, or impossible scenarios as if they were true.

This happens because AI models predict the most statistically likely next words based on patterns in training data, not by verifying facts. They can mix up information, create fictional entities, or generate technically impossible code that looks correct.

For AI builders, understanding and mitigating hallucinations is crucial. Techniques include using RAG to ground responses in real data, implementing fact-checking mechanisms, and designing prompts that reduce the likelihood of hallucinations.

An AI workflow is a sequence of connected steps where AI processes data, makes decisions, and triggers actions to complete complex tasks. It combines multiple AI capabilities, tools, and logic to automate end-to-end processes.

Workflows can include AI-powered steps like content generation, data extraction, and decision-making, along with traditional automation like data transformation, API calls, and conditional logic. They enable building sophisticated applications that handle real-world complexity.

For AI builders, workflows are the blueprint for production AI systems. They provide structure, reliability, and observability for AI operations, turning experimental AI capabilities into dependable business processes.

An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate with each other. For AI builders, APIs are the primary way to integrate AI capabilities into applications.

AI APIs like those from OpenAI, Anthropic, or Google provide access to powerful language models without needing to run them locally. You send a request with your prompt and parameters, and receive the AI's response, typically in JSON format.

Understanding APIs is fundamental for AI development - from authentication and rate limiting to handling responses and errors. They enable builders to add AI features to any application, regardless of programming language or platform.

Chain-of-thought (CoT) is a prompting technique where AI models show their reasoning process step-by-step before arriving at a final answer. It improves accuracy on complex problems by breaking them down into intermediate reasoning steps.

By explicitly asking the AI to 'think step by step' or 'show your work,' you can dramatically improve performance on tasks requiring logic, math, or multi-step reasoning. The AI explains its thought process, making outputs more interpretable and errors easier to spot.

For AI builders, chain-of-thought is a powerful technique for improving reliability and transparency. It's particularly valuable for applications requiring complex reasoning, problem-solving, or when you need to understand how the AI arrived at its conclusions.

Chunking is the process of breaking large documents or text into smaller, manageable pieces that fit within AI model token limits and optimize retrieval accuracy. It's crucial for processing long documents and implementing effective RAG systems.

The art of chunking involves finding the right balance - chunks must be small enough to fit token limits but large enough to maintain context. Common strategies include splitting by paragraphs, sentences, or using sliding windows with overlap.

For AI builders, effective chunking strategies directly impact application performance. Poor chunking can lead to lost context, irrelevant retrievals, or inefficient token usage. Good chunking preserves semantic meaning while optimizing for technical constraints.

Content generation refers to AI's ability to create various types of content - text, images, code, audio, or video - based on prompts or specifications. It's one of the most visible and transformative applications of generative AI.

Modern AI can generate articles, marketing copy, social media posts, product descriptions, code, designs, and more. The quality ranges from draft assistance to publication-ready content, depending on the model and prompting techniques used.

For AI builders, content generation capabilities enable creating tools for writers, marketers, developers, and creators. Understanding the strengths and limitations of different models helps build applications that augment human creativity rather than replace it.

Context engineering is the discipline of designing and optimizing what information an AI model sees at each step of its operation. It involves strategically selecting, formatting, and presenting context to maximize AI performance and accuracy.

This goes beyond simple prompt engineering to encompass memory management, information retrieval, and context prioritization. It's about ensuring the AI has exactly the right information it needs - no more, no less - at the right time.

For AI builders creating complex applications, context engineering is crucial for managing limited context windows, reducing hallucinations, and maintaining coherent multi-step workflows. It's the difference between an AI that occasionally works and one that reliably delivers.

The context window is the maximum amount of text or tokens an AI model can process and remember in a single conversation. It determines how much information from previous messages the AI can consider when generating responses.

Context windows vary significantly between models - from 4,000 tokens in earlier models to over 100,000 or even 1 million tokens in advanced systems. Larger context windows enable more complex conversations, analysis of longer documents, and maintaining coherence across extended interactions.

For AI builders, understanding context window limitations is crucial for designing effective applications. It affects how you structure prompts, manage conversation history, and chunk large documents for processing.

Embeddings are numerical representations of text that capture semantic meaning. They convert words, sentences, or documents into arrays of numbers that AI systems can process, compare, and search efficiently.

These vector representations enable semantic search - finding information based on meaning rather than exact keyword matches. Similar concepts have embeddings that are mathematically close together, allowing AI to understand relationships and context.

For AI builders, embeddings are crucial for implementing search, recommendation systems, and RAG applications. They enable efficient storage and retrieval of information, making it possible to build AI systems that understand and work with large amounts of textual data.

Few-shot prompting is a technique where you provide an AI model with a small number of examples before asking it to perform a task. These examples help the model understand the desired format, style, or approach without explicit instructions.

By showing 2-5 examples of input-output pairs, you can guide the AI to follow specific patterns, formats, or reasoning approaches. This is more effective than zero-shot prompting for complex or specialized tasks where the desired outcome isn't obvious.

For AI builders, few-shot prompting is essential for achieving consistent, high-quality outputs. It's particularly useful when you need specific formatting, domain-specific responses, or when working with tasks the model hasn't seen before.

Fine-tuning is the process of customizing a pre-trained AI model with your specific data to improve its performance on particular tasks or domains. It adjusts the model's parameters to better understand your unique requirements, terminology, or style.

Unlike training from scratch, fine-tuning starts with an existing model and refines it, requiring less data and computational resources. This makes it accessible for creating specialized AI models for specific industries, companies, or use cases.

For AI builders, fine-tuning offers a way to create models that consistently follow specific formats, understand domain-specific terminology, or maintain a particular tone. It's particularly valuable for enterprise applications requiring consistent, specialized outputs.

Function calling allows AI models to execute specific functions or tools based on natural language requests. The AI determines when and how to use available functions, extracting parameters from the conversation context.

This capability transforms AI from a text generator into an active agent that can interact with external systems. For example, an AI can call functions to search databases, perform calculations, send emails, or execute code based on user requests.

Function calling is fundamental for building AI agents and assistants that can take actions. It bridges the gap between AI's language understanding and real-world functionality, enabling more powerful and practical applications.

JSON (JavaScript Object Notation) is a lightweight data format that's become the standard for API communication and structured data exchange in AI applications. It's human-readable and easily parsed by machines, making it ideal for AI integrations.

AI models can generate and parse JSON, enabling structured outputs that integrate seamlessly with applications. This is crucial for building reliable AI systems that need to produce consistent, programmable outputs rather than free-form text.

For AI builders, JSON is the lingua franca of AI APIs. Understanding JSON structure, validation, and manipulation is essential for working with AI services, handling responses, and building robust integrations.

KV-cache (Key-Value cache) is a memory optimization technique in AI models that stores previously computed attention keys and values to avoid redundant calculations. It significantly improves inference speed and reduces computational costs.

The cache stores intermediate results from the model's attention mechanism, allowing it to reuse computations from previous tokens rather than recalculating everything. This is especially important for long conversations or when processing large contexts.

For AI builders, understanding KV-cache is important for optimizing AI application performance and costs. High cache hit rates mean faster responses and lower computational expenses, making it a crucial metric for production AI systems.

Model Context Protocol (MCP) is an open standard introduced by Anthropic that provides a universal way for AI systems to connect with external tools, data sources, and services. Think of it as USB-C for AI - a standardized connection method.

MCP enables AI applications to dynamically discover and interact with available tools without hard-coded integrations. It supports two-way communication, allowing AI to not just request data but also receive updates and notifications from connected systems.

For AI builders, MCP simplifies integration work significantly. Instead of building custom connections for each tool, you can use MCP-compatible servers that provide standardized access to databases, APIs, file systems, and other resources.

Memory management in AI systems refers to techniques for maintaining context and information across conversations or sessions. Since most AI models are stateless, implementing effective memory is crucial for coherent, contextual interactions.

Strategies include conversation history management, selective memory retention, and external memory stores. The challenge is balancing what to remember with context window limitations and relevance to current tasks.

For AI builders, memory management is essential for creating AI assistants that feel intelligent and aware. It involves decisions about what to store, how to retrieve relevant memories, and when to forget information to stay within constraints.

Multimodal AI refers to models that can process and generate multiple types of data - text, images, audio, video, and code. These systems understand and create content across different formats, enabling richer, more versatile applications.

Examples include models that can analyze images and answer questions about them, generate images from text descriptions, or transcribe and summarize videos. This versatility opens new possibilities for AI applications that mirror human perception and communication.

For AI builders, multimodal capabilities enable creating more intuitive, powerful applications. From visual debugging tools to content creation platforms, multimodal AI breaks the limitation of text-only interactions.

Prompt engineering is the art and science of crafting effective instructions to get better responses from AI models. It involves understanding how to communicate with AI systems to achieve desired outputs, whether for coding, content creation, or problem-solving.

Good prompt engineering includes being specific about requirements, providing context, giving examples when needed, and iterating based on responses. It's a crucial skill for AI builders because the quality of AI output heavily depends on the quality of the input prompts.

As AI models become more capable, prompt engineering evolves from simple commands to sophisticated techniques like chain-of-thought reasoning, role-playing, and structured output formatting. It's the primary interface between human intent and AI capability.

Prompt injection is a security vulnerability where malicious inputs manipulate an AI system to ignore its instructions or perform unintended actions. Attackers craft prompts that override system prompts or trick the AI into revealing sensitive information or executing harmful commands.

Common attacks include attempts to make AI ignore safety guidelines, reveal system prompts, or access data it shouldn't. As AI systems gain more capabilities and access to tools, prompt injection becomes a critical security concern.

For AI builders, understanding and preventing prompt injection is essential for building secure applications. Techniques include input validation, output filtering, and sandboxing AI capabilities to limit potential damage from successful attacks.

RAG is a technique that enhances AI responses by connecting language models to external knowledge sources. Instead of relying solely on training data, RAG retrieves relevant information from databases, documents, or APIs and uses it to generate more accurate, up-to-date responses.

The process involves converting documents into embeddings, storing them in vector databases, and retrieving relevant chunks based on user queries. This grounded approach significantly reduces hallucinations and enables AI to work with proprietary or recent information.

For AI builders, RAG is essential for creating applications that need accurate, domain-specific knowledge. It's the foundation for AI-powered documentation search, customer support bots, and knowledge management systems.

Rate limiting is a restriction on how many requests you can make to an AI API within a specific time period. It prevents system overload and ensures fair usage across all users of the service.

AI APIs typically have different types of limits: requests per minute, tokens per minute, and daily limits. Hitting these limits results in errors that can break your application if not handled properly. Premium tiers usually offer higher limits.

For AI builders, understanding and managing rate limits is crucial for building reliable applications. This includes implementing retry logic, request queuing, and potentially load balancing across multiple API keys or services.

Semantic search uses AI to understand the meaning and intent behind queries, rather than just matching keywords. It finds relevant information based on conceptual similarity, even when different words are used to express the same idea.

This technology powers modern AI search systems, using embeddings to convert text into numerical representations that capture meaning. Documents with similar meanings have similar embeddings, enabling finding relevant content regardless of exact wording.

For AI builders, semantic search is transformative for building intelligent applications. It enables natural language queries, better documentation search, and more accurate information retrieval, forming the foundation of many AI-powered features.

A system prompt is a set of initial instructions given to an AI model that defines its role, behavior, constraints, and response style. It acts as the AI's operating instructions, shaping every interaction that follows.

System prompts are hidden from end users but fundamentally influence how the AI responds. They can specify the AI's personality, expertise level, output format, and ethical boundaries. Well-crafted system prompts are crucial for creating consistent, reliable AI applications.

For AI builders, system prompts are the primary tool for customizing AI behavior without fine-tuning. They enable creating specialized assistants, ensuring consistent formatting, and implementing safety guidelines across all interactions.

Temperature is a parameter that controls the randomness and creativity of AI model outputs. Lower temperatures (0-0.3) produce more predictable, focused responses, while higher temperatures (0.7-1.0) generate more diverse, creative outputs.

At temperature 0, the AI always chooses the most likely next token, resulting in deterministic outputs. Higher temperatures increase the probability of selecting less likely tokens, introducing variety but potentially reducing accuracy or coherence.

For AI builders, temperature is a crucial parameter for tuning AI behavior. Use low temperature for factual tasks, coding, or when consistency is important. Use higher temperature for creative writing, brainstorming, or when variety is desired.

Tokens are the basic units that AI models use to process text. They can be words, parts of words, or even individual characters. Understanding tokens is essential for AI builders because they affect both cost and performance.

On average, one token equals about 4 characters in English text, or roughly 3/4 of a word. Most AI APIs charge based on token usage, and models have token limits for input and output. For example, a 1,000-word document might use approximately 1,300 tokens.

Knowing how tokenization works helps optimize prompts, manage costs, and stay within model limits. Different models may tokenize the same text differently, affecting both pricing and context window usage.

Token limit is the maximum number of tokens an AI model can process in a single request, including both input and output. It's a hard constraint that affects how much information you can provide and receive from the AI.

Different models have different limits - from 4,000 tokens in GPT-3.5 to 128,000 in GPT-4 Turbo or even 1 million in Gemini. Exceeding these limits results in errors or truncated responses, requiring strategies like chunking or summarization.

For AI builders, managing token limits is essential for application design. It affects how you structure conversations, process documents, and manage costs, since most AI services charge per token used.

Tool use refers to an AI's ability to interact with external tools, APIs, and services to complete tasks. It extends AI capabilities beyond text generation to actions like web searches, calculations, code execution, or database queries.

Modern AI models can determine when to use tools, which tools to use, and how to interpret results. This transforms AI from a conversational partner into an active agent capable of real-world interactions and task completion.

For AI builders, implementing tool use is key to creating practical AI applications. It requires defining available tools, handling authentication, managing errors, and ensuring the AI uses tools appropriately and safely.

A vector database is a specialized storage system designed to efficiently store, index, and search embeddings (numerical representations of data). It enables semantic search and is fundamental to RAG systems and AI applications working with large amounts of unstructured data.

Unlike traditional databases that match exact values, vector databases find similar items based on mathematical distance between embeddings. This allows finding relevant information based on meaning rather than keywords, crucial for AI applications.

For AI builders, vector databases are essential infrastructure for building scalable AI applications. They enable efficient semantic search, recommendation systems, and RAG implementations that can handle millions of documents while maintaining fast response times.

Vibe coding is an AI-assisted software development technique where developers describe what they want to build in natural language and iterate with AI to generate code. Unlike traditional programming, the developer focuses on the desired outcome rather than the implementation details, letting AI handle the code generation while providing feedback and refinements.

This approach revolutionizes development by allowing builders to create software through conversation with AI. The developer acts as a director, guiding the AI through iterations based on results rather than examining every line of code. It's particularly effective for prototyping, building personal tools, and exploring new ideas quickly.

The term gained prominence in 2025 when Andrej Karpathy popularized it, describing a workflow where developers primarily interact with AI through natural language, testing outputs, and requesting improvements without diving deep into the generated code itself.

A webhook is an automated message sent from one app to another when a specific event occurs. Unlike APIs where you request data, webhooks push data to your application automatically, enabling real-time integrations and workflows.

In AI applications, webhooks enable event-driven architectures. For example, when an AI finishes processing a task, completes a generation, or detects specific content, it can automatically notify other systems. This is crucial for building responsive, automated AI workflows.

Webhooks are essential for AI builders creating integrations between AI services and other tools, enabling automated pipelines where AI responses trigger subsequent actions in other applications.

Workflow engineering is the practice of designing, implementing, and optimizing multi-step AI processes that reliably complete complex tasks. It involves orchestrating AI calls, tool usage, decision logic, and error handling into robust, production-ready systems.

This discipline combines prompt engineering, system design, and traditional software engineering to create AI workflows that are predictable, observable, and maintainable. It's about turning experimental AI capabilities into dependable business processes.

For AI builders, workflow engineering is critical for moving beyond prototypes to production systems. It requires thinking about edge cases, failure modes, monitoring, and performance optimization to ensure AI workflows meet real-world requirements.

Zero-shot prompting involves asking an AI model to perform a task without providing any examples. The model relies entirely on its training and the instructions in your prompt to understand and complete the task.

This approach works well for straightforward tasks that the model likely encountered during training. It's the simplest form of prompting - just describe what you want and let the AI figure out how to do it.

For AI builders, zero-shot prompting is the starting point for most interactions. It's fast and requires no setup, making it ideal for prototyping, simple tasks, or when you want to test an AI's baseline capabilities before optimizing with examples.