Fine-Tuning
The process of further training a pre-trained model on domain-specific data to improve its performance on targeted tasks.
Fine-tuning takes a general-purpose model and adapts it to a specific domain by continuing training on curated examples. This can improve accuracy on specialized terminology, formatting conventions, and domain-specific reasoning patterns.
While fine-tuning can boost quality for niche use cases, it adds cost and complexity. For most document intelligence applications, well-engineered RAG pipelines with strong prompts achieve comparable results without the overhead of maintaining custom model weights.
More ai/ml Terms
Retrieval-Augmented Generation (RAG)
An AI architecture that combines information retrieval with text generation to produce answers grounded in source documents.
Vector Embedding
A numerical representation of text as a high-dimensional vector, enabling semantic similarity comparisons between passages.
BM25
A probabilistic keyword-ranking algorithm that scores documents by term frequency and inverse document frequency.
Chunking
The process of splitting large documents into smaller, overlapping segments optimized for retrieval and embedding.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
Large Language Model (LLM)
A neural network trained on massive text corpora that can understand and generate human language.
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