AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
Self-Consistency
Self-consistency is a prompting technique that generates multiple reasoning paths for the same problem and selects the most common final answer.
Prompt Chaining
Prompt chaining is a technique that breaks complex tasks into sequential steps, where each prompt builds on the output of the previous one.
Prompt Template
A prompt template is a reusable prompt structure with placeholder variables that gets filled with specific data at runtime for consistent AI interactions.
Role Prompting
Role prompting assigns a specific persona or expertise to a language model, causing it to respond as if it were that character or specialist.
Meta-Prompting
Meta-prompting uses a language model to generate, evaluate, or improve prompts, automating the prompt engineering process itself.
Prompt Injection
Prompt injection is a security vulnerability where malicious user input overrides system prompt instructions, causing the model to behave unexpectedly.
Jailbreaking
Jailbreaking is the practice of crafting prompts that bypass AI safety guardrails and alignment, making the model produce outputs it was trained to refuse.
Prompt Compression
Prompt compression reduces the token count of prompts while preserving essential meaning, fitting more context into limited context windows.
Pre-training
Pre-training is the initial phase of training a language model on vast amounts of text data to learn general language understanding and generation capabilities.
Next-Token Prediction
Next-token prediction is the core training objective of most LLMs, where the model learns to predict the most likely next token in a sequence of text.
Supervised Fine-Tuning
Supervised fine-tuning (SFT) trains a pre-trained model on labeled input-output pairs to specialize it for specific tasks or improve its response quality.
RLHF
RLHF (Reinforcement Learning from Human Feedback) is a training technique that aligns AI models with human preferences using feedback from human evaluators.
DPO
DPO (Direct Preference Optimization) is a simplified alternative to RLHF that directly optimizes language models on preference data without a separate reward model.
Reward Model
A reward model is a neural network trained to predict human preferences, scoring language model outputs to guide alignment training via RLHF.
Preference Data
Preference data consists of human comparisons between AI responses, indicating which response is better, used to train reward models and align language models.
Alignment
Alignment is the process of ensuring AI models behave in accordance with human values, intentions, and safety requirements.
Scalable Oversight
Scalable oversight refers to techniques for supervising AI systems effectively even as they become more capable than human evaluators at specific tasks.
RLAIF
RLAIF (Reinforcement Learning from AI Feedback) replaces human evaluators with AI models to generate preference data for alignment training.
PPO
PPO (Proximal Policy Optimization) is a reinforcement learning algorithm commonly used in RLHF to optimize language models based on reward model scores.
Human Feedback
Human feedback is the evaluative input from people used to train and align AI models, typically through preference comparisons or quality ratings.
LoRA
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that trains small adapter matrices instead of modifying all model weights.
QLoRA
QLoRA combines quantization with LoRA, enabling fine-tuning of large models on a single consumer GPU by using 4-bit quantized base weights.
Adapter
An adapter is a small, trainable module inserted into a pre-trained model that allows task-specific customization without modifying the original weights.
Prefix Tuning
Prefix tuning prepends trainable continuous vectors to model input, learning task-specific prefixes that steer the frozen model toward desired behavior.
Prompt Tuning
Prompt tuning learns soft prompt embeddings prepended to model input, optimizing continuous vectors that replace hand-crafted text prompts.
Parameter-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) encompasses methods that adapt pre-trained models by training only a small fraction of parameters, reducing cost and compute.
Full Fine-Tuning
Full fine-tuning updates all parameters of a pre-trained model on new data, providing maximum customization but requiring significant compute resources.
Layer Freezing
Layer freezing is a fine-tuning strategy that keeps certain model layers fixed while training others, balancing customization with preserved general knowledge.
Continued Pre-training
Continued pre-training extends the original pre-training process on domain-specific data, giving the model deep knowledge in a specialized area.
DoRA
DoRA (Weight-Decomposed Low-Rank Adaptation) improves on LoRA by separately adapting the magnitude and direction of weight matrices for better fine-tuning quality.
Long Context
Long context refers to language models capable of processing very large inputs, typically 100K tokens or more, enabling analysis of entire documents or codebases.
Sliding Window Attention
Sliding window attention limits each token to attend only to a fixed window of nearby tokens, reducing computation while maintaining local context.
In-Context Learning
In-context learning is the ability of language models to learn new tasks from examples or instructions provided in the prompt, without any parameter updates.
Context Extension
Context extension refers to techniques that increase a model pre-trained context window beyond its original training length without full retraining.
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) combines information retrieval with text generation, letting AI answer from external knowledge rather than just training data.
Paged Attention
Paged attention manages KV cache memory in non-contiguous blocks inspired by OS virtual memory, dramatically reducing waste and enabling more concurrent requests.
Scaling Law
Scaling laws are empirical relationships showing how model performance predictably improves with increases in model size, training data, and compute.
Chinchilla Scaling
Chinchilla scaling refers to the optimal ratio of model parameters to training tokens, showing most models were under-trained relative to their size.
Emergent Ability
An emergent ability is a capability that appears in large language models only above a certain scale threshold, absent in smaller models.
Mixture of Experts
Mixture of Experts (MoE) is a model architecture that uses multiple specialized sub-networks, routing each input to only a subset for efficient computation.
Sparse Model
A sparse model activates only a fraction of its total parameters for each input, achieving high capacity with lower computational cost per inference.
Unigram Tokenizer
A subword tokenization algorithm that starts with a large vocabulary and iteratively prunes it to find the optimal set of subword units.
Vocab Size
The total number of unique tokens in a language model tokenizer vocabulary, typically ranging from 30,000 to 100,000 or more.
EOS Token
The End-of-Sequence token is a special token that signals the model to stop generating text.
BOS Token
The Beginning-of-Sequence token is a special token placed at the start of input to signal the beginning of a new text sequence.
Pad Token
A special token used to fill shorter sequences to a uniform length so that batches of inputs can be processed together efficiently.
Mask Token
A special token used in masked language models like BERT that replaces a word so the model can learn to predict it from surrounding context.
Byte-Level BPE
A variant of byte-pair encoding that operates on raw bytes instead of Unicode characters, enabling tokenization of any text without unknown tokens.
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What is InsertChat?
InsertChat is a white-label AI assistant for your website. Train it, brand it, publish it, and learn from visitor questions.
How does InsertChat use my website content?
Connect approved pages, docs, videos, FAQs, policies, and other sources. InsertChat turns them into source-backed answers and next steps.
Can I control the assistant's tone and sources?
Yes. Choose its sources, tone, welcome message, and prompts so it stays on brand.
How does InsertChat stay accurate?
Answers use approved content and source links. Analytics show unclear or missing answers so you can improve coverage.
Can it collect leads or route support questions?
Yes. InsertChat can collect details, qualify intent, add context, and send chats to the right inbox, CRM, workflow, or person.
Can I control how the assistant behaves?
Yes. Control prompts, model choice, tool access, and the branded assistant experience so behavior stays consistent.
Which AI models can I use?
InsertChat supports multiple model providers. Choose each assistant's model for quality, speed, and cost, or use BYOK.
Can I pick different models for different workflows?
Yes. Use a faster model for common questions and a stronger model for complex reasoning. InsertChat supports that balance per conversation.
Where can I deploy an assistant?
Use a widget, embed, full-page assistant, custom domain, in-app embed, or API. Reuse one setup across surfaces.
Do I need coding skills?
No. Build and deploy AI assistants using our visual builder. The embed code is one line of JavaScript.
Can I customize the branding and UI?
Yes. Customize the assistant name, logo, colors, welcome message, suggested prompts, tone, domain, and white-label presentation.
Can I use my own domain?
Yes. Custom domains are supported, typically via enterprise options.
Does InsertChat support voice?
Yes. Voice dictation and text-to-speech let users speak instead of type.
Does InsertChat support vision?
Yes. Enable vision for assistants when images help clarify a request or context.
What tools and integrations are supported?
Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
Can I control which tools the assistant is allowed to use?
Yes. Tool access is controlled per assistant so you enable only what you need.
Can the agent hand off to a human?
Yes. Configure human handoff so the agent escalates when needed. Full conversation history is passed along.
Do you provide analytics?
Yes. Track chats, leads, feedback, top questions, unanswered questions, most-used sources, and content gaps.
Is it mobile friendly?
Yes. The widget and embeds work well on desktop and mobile with no separate experience needed.
What's the fastest path to a successful deployment?
Start with one assistant and a small set of high-value sources. Iterate using real questions from analytics.
What is the fastest way to get started?
Create an account. Connect one key source. Ask a test question, brand the assistant, then publish it on one page.