Glossary

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AI Alignment
The process of ensuring that AI systems behave in ways that are consistent with human values and intentions.
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AI Interpretability
The degree to which a model’s decision-making process can be understood by humans.
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Adversarial prompting
Designing prompts to test or exploit vulnerabilities in AI models.
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Agent Swarm
An experimental, educational framework for exploring ergonomic, lightweight multi-agent orchestration.
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Attention mechanism
A technique that allows models to focus on different parts of the input when generating output.
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Chain-of-thought prompting
Chain-of-thought prompting is a strategy that encourages an AI model to articulate its reasoning process step-by-step. This method often leads to more accurate and transparent decision-making.
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Constitutional AI
Techniques to align AI models with specific values or principles through careful prompt design.
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Constrained generation
Using prompts to limit the model’s output to specific formats or content types.
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Context window
The maximum amount of text a model can process in a single prompt.
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Cross-task generalization
The ability of a model to apply knowledge from one type of prompt to a different but related task.
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Dynamic Agents
AI agents that can adapt their behavior and instructions based on context and previous interactions.
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Embeddings
Dense vector representations of words, sentences, or other data types in a high-dimensional space.
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Explainable AI
AI systems designed to provide clear explanations for their outputs or decisions.
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Feature engineering
The process of selecting, modifying, or creating new features from raw data to improve the performance of machine learning models.
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Federated learning
A machine learning technique that trains algorithms across multiple decentralized devices or servers holding local data samples.
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Few-shot prompting
Few-shot prompting is a method that involves providing a small number of examples to guide an AI model's performance on a task.
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Fine-tuning
The process of further training a pre-trained model on a specific dataset to adapt it to a particular task or domain.
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Function Calling
Enabling AI models to call specific functions to perform tasks or retrieve information.
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Generative Adversarial Networks (GANs)
A framework where two neural networks (a generator and a discriminator) compete against each other to create realistic data.
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Gradient Descent
An optimization algorithm used to minimize the cost function in machine learning by iteratively updating the model parameters.
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Hallucination (AI)
When an AI model generates false or nonsensical information that it presents as factual.
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In-context learning
The model’s ability to adapt to new tasks based on information provided within the prompt.
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Instruction tuning
Fine-tuning language models on datasets focused on instruction-following tasks.
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Knowledge Distillation
A machine learning technique that aims to transfer the learnings of a large pre-trained model (the "teacher model") to a smaller "student model."
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