Key Takeaways
- Artificial Intelligence (AI) is a relatively new and rapidly evolving field, which has led to the development of unique jargon and terms.
- These terms are often used to convey complex ideas concisely and efficiently.
A
Active Workflow – A workflow that has been saved and activated, meaning its trigger is live and will run automatically when the trigger condition is met.
AI Agent – A system (built using n8n workflows) that perceives input, makes decisions (workflow logic) and takes actions (calls tools/APIs, generates responses) to achieve a goal. It often involves multiple steps and interactions with LLMs and other services.
API (Application Programming Interface) – The method provided by AI service providers (like OpenAI, Google, Anthropic) and other tools allows applications like n8n to interact with them programmatically.
API Key – A unique secret code to authenticate your n8n workflow when requesting an external service’s API (like an LLM). Stored securely in n8n Credentials.
C
Chat History / Memory – Maintaining the context of a conversation over multiple turns. It can range from simple pass-through in prompts to complex storage and retrieval using n8n variables or external databases.
Community Nodes – Custom nodes developed by the n8n community, extending n8n’s built-in functionality. These might offer integrations with newer AI services or specialized tools.
Completion / Response – The output text the LLM generates in response to your prompt. This arrives as data (usually within a JSON object) in your n8n workflow.
Conditional Logic (IF Node / Switch Node) – Nodes allow your workflow (agent) to take different paths based on specific criteria or data values (e.g., analyzing sentiment and checking function call requests).
Connection / Edge – The line connecting two nodes in the workflow editor, indicating the flow of data from one node’s output to the next node’s input.
Context Window – The maximum number of tokens an LLM can consider simultaneously (input prompt + generated output). Managing this is crucial for complex conversations or large documents.
Credentials – Securely stored authentication information (API keys, tokens, user/pass) needed for n8n nodes to connect to external services.
CSV / File Handling Nodes – Nodes used to read, parse, or write data in various file formats (like CSV, JSON, and Binary). Useful for agents that need to process documents or data exports/imports.
D
Data Transformation – The process of modifying data from one format or structure to another using n8n nodes (like Item Lists, Set, Function) to make it suitable for subsequent steps, especially for preparing data for an LLM or processing its response.
E
Embedding – A numerical vector representation of text capturing its semantic meaning. Generated via AI services (using specific n8n nodes) and used for semantic search, clustering, or as input to some models.
Endpoint – A specific URL within an API corresponding to a particular function or resource (e.g., the “completions” endpoint for an LLM API). Often specified in HTTP Request nodes or within dedicated service nodes.
Environment Variables – Variables configured outside the workflow (in the n8n environment settings) are used to store configuration details like API keys or base URLs, promoting better security and manageability.
Error Handling / Error Workflow – Mechanisms within n8n (like “Continue On Fail” setting or dedicated Error Triggers/Workflows) to manage and respond to errors gracefully during workflow execution, making the agent more robust.
Execution – A single run of an n8n workflow from its trigger to completion or error.
Expression – A snippet of JavaScript code within node parameters to dynamically access, manipulate, or reference data flowing through the workflow. Essential for using data from previous steps.
F
Function Calling / Tools – The capability of advanced LLMs to request the execution of predefined functions/tools based on the prompt. n8n workflows can define and execute the logic for these tools when requested by the LLM.
Function Node / Code Node – Nodes allowing custom JavaScript code for complex data manipulation, logic, or custom API interactions not covered by standard nodes.
H
Hallucination – When an LLM generates plausible-sounding but factually incorrect, irrelevant, or nonsensical information.
HTTP Request Node – A versatile n8n node used to interact with almost any REST API, including AI services that might not have a dedicated n8n node.
I
Input Data / Output Data – The data a node receives from the previous step (input) and the data it produces for the next step (output). Inspectable in the n8n editor.
J
JSON (JavaScript Object Notation) – The standard data interchange format in n8n. LLM API responses are typically in JSON.
L
LangChain / LlamaIndex Nodes – Specific n8n nodes integrating with popular AI development frameworks, potentially simplifying the construction of complex patterns like RAG or agents within n8n.
Large Language Model (LLM) – The core AI engine (e.g., GPT series, Claude series, Gemini series) that processes and generates text. Accessed via API calls within n8n.
Latency – The time delay between sending a request (e.g., a prompt to an LLM) and receiving the response. An important consideration for real-time agent interactions.
Looping (e.g., SplitInBatches, Loop Over Items) – n8n constructs for processing multiple items or performing actions iteratively, essential for tasks like processing data lists with an AI.
M
Manual Execution – Running a workflow directly from the n8n editor for testing and development purposes.
Max Tokens – A parameter set in LLM API calls to limit the generated response’s maximum length (number of tokens). Controls cost and response verbosity.
Merge Node – Combines data streams from different branches in a workflow, often used after conditional logic or parallel processing.
Model Parameters (e.g., Frequency Penalty, Presence Penalty) – Additional settings (beyond temperature/top-p) available in some LLM APIs to fine-tune the output, such as discouraging repetition.
Multimodal Models – AI models capable of processing and understanding multiple types of data input, such as text and images (e.g., GPT-4 Vision, Gemini). Interacting with these might require specific image data handling (like base64 encoding) in n8n.
N
Node – A fundamental building block in an n8n workflow, performing a single specific action (trigger, API call, data manipulation, logic).
n8n – It is a low-code, open-source workflow automation platform that allows users to create custom integrations and automate tasks across different applications without extensive coding.
P
Planning (by LLM) – An agent capability where the LLM itself breaks down a complex task into a sequence of steps or sub-goals, which the n8n workflow can then execute.
Prompt – The input text query, instruction, or conversation is sent to an LLM to elicit a desired response.
Prompt Engineering – The skill and practice of designing effective prompts to guide the LLM towards accurate, relevant, and well-formatted outputs.
R
RAG (Retrieval-Augmented Generation) – An AI technique where relevant information is first retrieved (often from a vector database) and then provided as context to the LLM to generate a more informed and grounded response. Built in n8n using vector DB, embedding, and LLM nodes.
Rate Limiting – Restrictions imposed by API providers on the number of requests allowed within a specific period. Agents need to respect these limits, possibly using Wait nodes or error handling in n8n.
Reasoning / Chain-of-Thought Prompting – Prompting techniques that encourage the LLM to “think step-by-step” or explain its reasoning process before giving a final answer, often improving accuracy on complex tasks. The n8n workflow sends such structured prompts.
Regular Node – Any node in an n8n workflow that is not a Trigger node; it performs an action based on the received input.
S
State Management – The process of keeping track of information (the agent’s “state”) across different steps or executions of a workflow. This can involve passing data between nodes, using n8n Static Data, or external databases for persistence.
Static Data – A simple, built-in key-value store within n8n workflows, useful for persisting small amounts of data or state between executions (use with caution for concurrency).
Streaming Response – Handling the output from an LLM as it’s being generated (token by token or chunk by chunk) rather than waiting for the entire response. Useful for perceived performance in chat applications, requires specific handling in n8n (potentially via Server-Sent Events triggers or custom code).
Sub-Workflow / Execute Workflow Node – Allows you to call one n8n workflow from within another, promoting modularity and reusability in complex agent designs.
System Prompt / Instructions – High-level instructions given to an LLM to define its persona, rules, context, or task objectives, usually persisting across multiple turns in a conversation. Configurable in most n8n LLM nodes.
T
Token – The fundamental unit of text processed by LLMs (word fragment, word, punctuation). API usage and context windows are measured in tokens.
Trigger Node – The node that initiates a workflow execution based on a defined event (schedule, webhook, manual start, etc.). Defines how the agent is activated.
Vector Database – A specialized database optimized for storing and querying high-dimensional vectors, primarily used for storing embeddings for fast semantic search in RAG systems. n8n offers nodes for interacting with popular vector DBs.
V
Versioning (Workflows) – n8n automatically saves previous versions of your workflows, allowing you to review changes and revert if necessary. It is crucial for maintaining complex agent logic during development.
W
Wait Node – An n8n node that pauses the workflow for a specified duration or until a specific time. Helpful in dealing with rate limits, waiting for asynchronous operations, or scheduling follow-up actions.
Webhook Node – This can act as a trigger (starts workflow when an external service sends data to a unique URL) or as a regular node (sends data to an external service). Essential for real-time integration and communication for agents.
Workflow – The entire automated process is designed on the n8n canvas, comprising connected nodes that represent the logic and actions of your AI agent.
Final Thoughts
Understanding the language is central to success when diving into new pursuits in the AI space. Before you can devise an effective strategy or select appropriate tools to help you build, train, and evaluate models, you should have a certain level of AI literacy. Hopefully, this article contributed to that effort by clarifying important terms and explaining essential concepts!
