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Foundational model

As explained in Financial AI in Practice

A foundation model is a large, broadly trained model that can be adapted or guided for many downstream tasks.

A foundation model is a broadly trained, pretrained model that can be steered toward specific business problems. In finance, the work shifts from training a separate model for each task to engineering the system around a general-purpose model and guiding it safely and effectively. section 3.1.1

From task-specific models to a shared engine

Market sentiment classification and research-report summarization once called for separate custom models. Foundation models shift the focus toward steering a general-purpose model for these kinds of tasks. section 3.1.1

How Financial AI in Practice covers it

How deep it goes
Defines the shift from task-specific training to AI engineering without detailing implementation steps.
Examples
Contrasts market-sentiment classification and research-report summarization as formerly separate modeling tasks.
What you can do after
Apply it by steering a general-purpose model safely and effectively toward a specific business problem.
How it connects
Connects foundation models with large language models, natural language processing, and AI engineering.

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This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.