Foundational model

As explained in Software Testing with Generative AI

A foundational model is a pretrained machine-learning model that provides a general base for further adaptation or specialization.

A foundational model is a pretrained machine-learning model used as a general base for further adaptation or specialization. Fine-tuning trains it with additional data to improve contextual understanding, change its tone, or target specific queries and instructions. section 12.1.1

Fine-tuning develops a foundational model through several connected activities:

• Set a specific goal for the adapted model. section 12.1.1
• Prepare relevant data and format it for training. section 12.3
• Tokenize training prompts while respecting the model’s context length. section 12.3
• Test the tuned model through automated inference or manual evaluation. section 12.3

Model families and products

GPT-3.5-Turbo and GPT-4o are used as foundation models for products including ChatGPT, GitHub Copilot, and Microsoft Bing AI. Gemini provides related model variants, including Gemini 1.5 Pro, Gemini 1.5 Flash, and Gemini 1.0 Pro. section 2.5.2

Figure 12.1 A visual representation of the different steps taken during fine-tuning

Use the process map to follow how goal setting, data preparation, processing, tuning, and testing feed into fine-tuning. section 12.1.1

How Software Testing with Generative AI covers it

How deep it goes

Defines the model and outlines fine-tuning, but defers detailed treatment to goals, data, tuning, and testing.

Examples

Uses GPT-3.5-Turbo, GPT-4o, ChatGPT, GitHub Copilot, Microsoft Bing AI, and Gemini variants as concrete examples.

What it assumes

Introduces the distinction between an existing trained model and additional training with prepared data.

What you can do after

Apply it via fine-tuning to adapt a model’s context, tone, or responses, then test the result.

How it connects

Connects foundational models to fine-tuning, tokenization, context length, inference, and model testing.

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Related concepts

• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• pretrained modelkind of · A pretrained model is a machine-learning model whose parameters were learned through prior training before it is used or adapted for a subsequent task.
• instruct modelincludes · An instruction-tuned model is a pretrained model further trained to follow natural-language instructions and produce task-appropriate responses.
• feature engineeringused by · Feature engineering is the creation, transformation, or selection of input features from data to provide useful representations for analysis or modeling.
• TimesFMincludes · TimesFM is a foundation model designed for deterministic time-series forecasting.

This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.