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.
Gemini Flash
Google's fast and efficient Gemini variant optimized for high-volume, cost-sensitive applications with strong multimodal capabilities.
Gemini Pro
The core model in Google's Gemini family, providing strong general-purpose performance with native multimodal understanding.
Gemini Ultra
The most capable model in Google's Gemini family, designed for the most complex reasoning and multimodal tasks.
Llama 3
Meta's third generation of open-weight language models, offering strong performance across 8B and 70B sizes for broad open-source adoption.
Llama 3.1
An enhanced version of Llama 3 with extended 128K context, multilingual support, and a new 405B parameter flagship model.
Mistral 7B
Mistral AI's efficient 7-billion-parameter model that outperformed much larger models at its release through architectural innovations.
Mixtral
Mistral AI's Mixture of Experts model that achieves performance rivaling much larger dense models while using only a fraction of parameters per token.
Phi-3
Microsoft's family of small language models that achieve strong performance through high-quality training data curation rather than scale.
Qwen 2
Alibaba's second-generation multilingual LLM family, offering competitive performance across multiple sizes with strong support for Chinese and English.
DeepSeek-V3
DeepSeek's third-generation MoE model with 671B total parameters achieving frontier performance at remarkably low training cost.
DeepSeek-R1
DeepSeek's reasoning model that uses reinforcement learning to develop strong chain-of-thought reasoning, competing with OpenAI's o1.
Command R
Cohere's retrieval-optimized language model designed for enterprise RAG applications with strong multilingual support and long context.
Command R+
The more powerful variant in Cohere's Command R family, offering stronger reasoning and generation while maintaining RAG optimization.
Grok-2
xAI's second-generation language model with strong reasoning capabilities and real-time access to information through the X platform.
Inference
The process of using a trained model to generate predictions or outputs from new inputs, as opposed to training the model.
Prefill
The initial phase of LLM inference where the entire input prompt is processed in parallel to populate the KV cache before token generation begins.
Time to First Token
The latency between sending a request and receiving the first token of the response, a key metric for user-perceived responsiveness.
Tokens Per Second
A measure of inference speed indicating how many tokens a model can generate per second, varying by hardware, model size, and optimization.
Model Distillation
A technique where a smaller student model is trained to mimic the outputs of a larger teacher model, transferring knowledge into a more efficient form.
Knowledge Cutoff
The date after which an LLM has no information, determined by when its training data collection ended.
Benchmark
A standardized test or dataset used to evaluate and compare language model performance across specific capabilities like reasoning, coding, or knowledge.
Attention Mechanism
A neural network component that dynamically focuses on relevant parts of the input when producing each output element, mimicking selective human attention.
Guardrails
Safety mechanisms and rules that constrain AI model behavior, preventing harmful, off-topic, or inappropriate outputs.
Safety Filter
An automated system that screens AI inputs and outputs for harmful, toxic, or policy-violating content and takes appropriate action.
AI Safety
The field focused on ensuring AI systems behave reliably, avoid causing harm, and remain aligned with human values and intentions.
Context Caching
A feature that caches the processed input context across multiple requests, reducing latency and cost for repeated prompts with shared prefixes.
Model Router
A system that automatically selects the best model for each query based on complexity, cost, and capability, optimizing quality and spending.
Prompt Caching
An API-level feature that stores processed prompt prefixes to reduce cost and latency for subsequent requests sharing the same prefix.
Tokenomics
The cost structure and pricing model for LLM API usage, typically based on input and output token counts with different per-token rates.
Latent Space
The high-dimensional internal representation space where a model encodes concepts, relationships, and knowledge during processing.
Fine-Tuning
The process of further training a pre-trained model on a specific dataset to improve its performance on a particular task or domain.
Batching
Processing multiple inference requests together in a single forward pass to maximize GPU utilization and throughput.
Tensor Core
Specialized hardware units in NVIDIA GPUs designed for accelerating matrix multiplication operations that are central to neural network computation.
Mixed Precision
A training technique that uses lower-precision number formats for most computations while keeping critical values in higher precision for accuracy.
Catastrophic Forgetting
A phenomenon where fine-tuning a model on new data causes it to lose previously learned knowledge and capabilities.
Training Data
The corpus of text used to train a language model, typically comprising trillions of tokens from books, websites, code, and other text sources.
Data Contamination
When benchmark evaluation data appears in the training data, artificially inflating model scores without reflecting genuine capability.
API Endpoint
A URL that applications call to send prompts to an LLM and receive generated responses, the standard interface for using AI models in production.
Rate Limiting
Restrictions on how many API requests or tokens can be processed within a given time window, protecting infrastructure and ensuring fair usage.
Zero-Shot Learning
The ability of a model to perform a task correctly without any task-specific examples, relying solely on its pre-trained knowledge and instructions.
Chain-of-Thought Reasoning
The explicit step-by-step reasoning process that models use to work through complex problems, improving accuracy on math, logic, and analysis tasks.
Natural Language Processing
The field of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.
Natural Language Understanding
The ability of an AI system to comprehend the meaning, intent, and context of human language input, beyond just processing the words.
Natural Language Generation
The AI capability of producing fluent, coherent human language text from structured data, prompts, or conversational context.
Sycophancy
The tendency of AI models to tell users what they want to hear rather than providing honest, accurate responses, especially when corrected or challenged.
Tool Use
The ability of an LLM to invoke external tools, APIs, or functions to access information and take actions beyond its training data.
Agentic Workflow
A task execution pattern where an AI agent autonomously plans and executes a series of steps, making decisions at each stage based on intermediate results.
Transfer Learning
The practice of using knowledge learned by a model on one task or domain to improve performance on a different but related task or domain.
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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?
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Can I control how the assistant behaves?
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Which AI models can I use?
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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?
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Can I use my own domain?
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Does InsertChat support voice?
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Does InsertChat support vision?
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Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
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Can the agent hand off to a human?
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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.