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.
Character-Level Tokenization
A tokenization approach that treats each individual character as a separate token, producing long sequences but requiring no vocabulary training.
Merge Rule
A rule in BPE tokenization that specifies which pair of tokens should be merged into a single new token, learned from training data frequency.
Tokenizer Training
The process of learning a tokenizer vocabulary and rules from a text corpus before the language model itself is trained.
Sampling Strategy
The method used to select the next token from a probability distribution during text generation, ranging from greedy to highly random approaches.
Length Penalty
A parameter used in beam search and other decoding methods to control whether the model favors shorter or longer generated sequences.
Min-p
A dynamic sampling method that filters out tokens with probabilities below a fraction of the most likely token probability.
Mirostat
An adaptive sampling algorithm that dynamically adjusts the sampling parameters to maintain a target level of surprise (perplexity) in generated text.
Typical Sampling
A sampling method that selects tokens whose information content is close to the expected information content, filtering out both too-obvious and too-surprising tokens.
One-Shot Prompting
A prompting technique that provides the model with exactly one example of the desired input-output format before the actual query.
Reflexion
A prompting framework where the model reflects on its own outputs, identifies errors, and uses that self-feedback to improve subsequent attempts.
Plan-and-Solve
A prompting strategy that instructs the model to first create a step-by-step plan and then execute each step, improving multi-step reasoning accuracy.
Least-to-Most Prompting
A prompting technique that breaks complex problems into simpler subproblems, solving them in order from easiest to hardest and building on each result.
Persona Prompting
A prompting technique that assigns a specific identity, expertise, or personality to the model to shape the style and content of its responses.
Automatic Prompt Optimization
The use of algorithms and AI to automatically discover, refine, and improve prompts for better LLM performance on specific tasks.
Directional Stimulus Prompting
A prompting framework that provides small, targeted hints or keywords to guide the model toward a desired output without specifying the full answer.
Step-Back Prompting
A prompting technique that asks the model to first consider a higher-level or more abstract version of the question before answering the specific query.
Skeleton-of-Thought
A prompting technique that first generates an outline skeleton of the answer, then expands each point in parallel, reducing end-to-end latency.
Masked Language Modeling
A pre-training objective where random tokens are masked and the model learns to predict them from surrounding context, used by BERT-style models.
Causal Language Modeling
A pre-training objective where the model learns to predict the next token given all previous tokens, used by GPT-style generative models.
Reward Hacking
When an AI model learns to exploit flaws in the reward signal to achieve high scores without actually performing the intended task well.
GRPO
Group Relative Policy Optimization is a reinforcement learning method that scores groups of model outputs against each other rather than using a separate reward model.
AdaLoRA
An adaptive variant of LoRA that dynamically allocates the rank of low-rank adaptation matrices based on the importance of each weight matrix.
LongLoRA
An efficient fine-tuning method that extends the context length of pre-trained models using shifted sparse attention and LoRA, requiring minimal additional compute.
P-Tuning
A parameter-efficient method that prepends learnable continuous embeddings to the input, trained with an LSTM-based prompt encoder for better optimization.
IA3
Infused Adapter by Inhibiting and Amplifying Inner Activations, a PEFT method that scales model activations with learned vectors, using even fewer parameters than LoRA.
BitFit
A parameter-efficient fine-tuning method that only updates the bias terms in a pre-trained model, leaving all weight matrices frozen.
Context Length
The number of tokens a model can process in a single forward pass, synonymous with context window size.
RoPE Scaling
Techniques for extending the context length of models using Rotary Position Embeddings by modifying the frequency or interpolation of position encodings.
YaRN
Yet another RoPE extensioN, an advanced method for extending model context length that combines NTK-aware interpolation with attention scaling.
ALiBi
Attention with Linear Biases, a position encoding method that adds a linear distance-based penalty to attention scores, enabling length generalization.
StreamingLLM
A framework that enables LLMs to handle infinite-length sequences by retaining attention sinks and a sliding window of recent tokens.
KV Cache Compression
Techniques that reduce the memory footprint of the key-value cache during inference, enabling longer sequences and higher throughput.
Multi-Query Attention
An attention variant where all attention heads share a single set of key and value projections while maintaining separate queries, dramatically reducing KV cache size.
Flash Decoding
An optimized algorithm for the decoding phase of LLM inference that parallelizes attention computation across the KV cache sequence dimension.
Dynamic Batching
An inference optimization that groups incoming requests into batches dynamically based on arrival time, maximizing GPU utilization.
Model Size
The total number of parameters in a neural network, typically measured in billions for modern LLMs, determining capacity and computational requirements.
Parameter Count
The total number of trainable weights and biases in a neural network, serving as a primary measure of model capacity and complexity.
Dense Model
A neural network where all parameters are active for every input, in contrast to sparse models where only a subset of parameters is used per token.
Top-k Routing
The mechanism in Mixture of Experts models that selects the top-k most relevant experts for each input token based on a learned routing function.
Expert Parallelism
A model parallelism strategy where different experts in a Mixture of Experts model are placed on different GPUs, enabling efficient distributed inference.
Load Balancing Loss
An auxiliary training loss that encourages even distribution of tokens across experts in Mixture of Experts models, preventing expert collapse.
Over-training
Deliberately training a model on more data than is compute-optimal according to scaling laws, to produce a smaller model that is cheaper to serve at inference time.
GPT-4o Mini
A smaller, faster, and cheaper variant of GPT-4o designed for high-volume tasks that need good quality at lower cost.
o1
OpenAI's reasoning model that uses extended "thinking" before responding, achieving breakthrough performance on math, coding, and science tasks.
o3
OpenAI's advanced reasoning model succeeding o1, with improved reasoning capabilities and efficiency across math, science, and coding tasks.
Claude 3 Haiku
The fastest and most compact model in Anthropic's Claude 3 family, optimized for speed and cost-efficiency in high-volume applications.
Claude 3 Sonnet
The balanced mid-tier model in Anthropic's Claude 3 family, offering strong performance with good speed and reasonable cost.
Claude 3 Opus
The most capable model in Anthropic's Claude 3 family, excelling at complex reasoning, nuanced analysis, and sophisticated generation tasks.
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Do you provide analytics?
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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.