Domain 3 of 4

Techniques to Improve Gen AI Model Output

The limits a foundation model arrives with, grounding against first party, third party and world data, prompting from zero shot through ReAct, the sampling parameters that shape a response and customisation.

Concepts

~20%

Of the exam

Practice questions

Concepts in this domain

01What generative AI is good and bad atThe capabilities worth building on, the failure modes that are properties of the technology and will outlast any model, and how to tell a suitable problem from an unsuitable one.02LLM Grounding Explained: Sources, Citations and HallucinationsLearn how LLM grounding connects answers to evidence, how it differs from RAG and how to check citations, source quality and unsupported claims.03What 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.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.

Try a question from this domain

An assistant must answer questions about legislation that changes several times a year, using current public information. Which grounding approach fits?

APrebuilt RAG with Agent Search over an internal copy of the legislation.
BFine tuning the model on the legislation each time it changes.
CRaising the context window so more of the legislation fits in each prompt.
DGrounding with Google Search, which ties answers to current public information.

12 questions on this domain.

One per page, with a worked explanation.

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