Wiki
Look up terms, definitions, and resources across AI, data, and development.
Fine-Tuning
The process of taking a pre-trained AI model and further training it on a smaller, domain-specific dataset to improve performance on particular tasks.
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Prompt Engineering
The practice of designing and optimising inputs to AI models to elicit accurate, useful, and consistently structured outputs.
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Retrieval-Augmented Generation
A technique that enhances LLM outputs by grounding them in external knowledge sources, dramatically reducing hallucinations and enabling domain-specific accuracy.
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Synthetic Data
Artificially generated data that mimics the statistical properties of real-world data, used for training AI models when real data is scarce, sensitive, or biased.
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Vector Database
A specialised database designed to store, index, and search high-dimensional vector embeddings, enabling semantic similarity search at scale.
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Neural Network
Computing systems inspired by biological neurons that learn patterns from data.
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Machine Learning
Systems that learn from data without being explicitly programmed for every case.
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Natural Language Processing
AI that enables machines to understand, generate, and work with human language.
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Artificial Intelligence
The field of building systems that perform tasks that typically require human intelligence.
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Agentic AI
AI systems that act autonomously to achieve goals with minimal step-by-step human direction.
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Deep Learning
Machine learning using multi-layer neural networks to learn hierarchical representations of data.
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Large Language Model
AI models trained on vast text data to understand and generate human-like language.
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Transformer
A neural architecture that uses attention to process sequences and is the basis of most LLMs.
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Computer Vision
AI that enables machines to interpret and understand images and video.
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Data Engineering
Designing and building systems that collect, store, and deliver data for analytics and ML.
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