Domain 3 of 5

Applications of Foundation Models

The heaviest domain. Choosing a model and setting its inference parameters, prompt engineering and the attacks against it, Retrieval Augmented Generation, the four ways to customise a model and how to evaluate the result.

Concepts

~28%

Of the exam

Practice questions

Concepts in this domain

01Choosing and configuring a foundation modelThe criteria that actually separate one model from another, and what temperature, top p and output length do to the answers that come back.02What Is Prompt Engineering? Techniques and ExamplesUnderstand prompt engineering with examples of zero shot and few shot prompting, prompt templates, chain of thought, evaluation and common security risks.03What Is RAG? Retrieval-Augmented Generation ExplainedLearn how RAG works, from chunking and embeddings to hybrid search, reranking and evaluation, with clear distinctions between recall and hit rate.04Customising a foundation modelThe five ways to close the gap between a general model and a specific problem, ordered by cost, and the question that decides which one a problem needs.05Evaluating a foundation modelROUGE, BLEU and BERTScore, what each one can and cannot see, why a public benchmark is a filter and no decision, and why human evaluation stays the reference the automated scores are checked against.

Try a question from this domain

A service extracts the invoice number, date and total from uploaded documents and passes the result to an accounting system. The output must be consistent, because a downstream parser depends on it. Which inference setting is most appropriate?

AA temperature at or near zero, so the model consistently picks the likeliest continuation.
BA high temperature, so the model considers more possible readings of the document.
CA high top p value, to widen the set of tokens the model may choose from.
DA raised maximum output length, so the model has room to correct itself.

18 questions on this domain.

One per page, with a worked explanation.

Start the set