Foundation Models, Retrieval and Grounding

All courses / AI Platform and Security / Module 2 / Chapter 10

Learning objectives

After the lesson the participant can:

• Compare the four build, buy or adapt options by what the organisation controls and its position under the AI Act.
• Match each failure mode of retrieval-augmented generation to its architectural remedy.
• Design a retrieval prompt template with grounding, machine-checkable citations and a fixed abstention text.
• Draft evaluation items for a retrieval system, including refusal and no-answer cases.

Key concepts

Foundation model

A model trained on broad data at scale that can be adapted to a wide range of downstream tasks. Its two architecturally significant properties are emergence, capabilities not explicitly designed, and homogenisation, many systems depending on a few models.

Retrieval-augmented generation (RAG)

A generative model combined with a non-parametric memory: a retriever selects passages from an indexed corpus and the generator conditions on the query and the passages. The output rests on sources the organisation controls, can update and can cite.

Grounding

The design response to hallucination: the prompt restricts answers to supplied passages, the output format requires a citation identifier after each claim, and a grounding check scores each claim against its passage and removes, flags or routes unsupported claims.

Structure-aware chunking

Splitting documents on headings and tables and keeping a heading path with each chunk. Every chunk also carries source identifier and version, position, access-control attributes, language and ingestion timestamp.

Refusal case

An evaluation item the system should decline. A set containing only questions the system should answer measures helpfulness alone; refusal and no-answer cases measure safety.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Teaching plan

Discussion

1. The chapter says the organisation's differentiating knowledge is almost never in the model. What follows for a proposal in your organisation to fine-tune a model on internal documents?
2. Citations turn an answer into a lead that can be checked. In which of your uses do people check the lead, and in which do they not?
3. Which questions would regulators and journalists try first on an assistant your organisation runs, and are they in its evaluation set?

Written exercise

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

1. Why does the citizen-service prompt template prescribe an exact abstention text instead of letting the model apologise in its own words?

   1. Because a fixed string can be detected and counted in evaluation; a free-form apology cannot
   2. Because Article 50(1) of the AI Act prescribes the wording a system must use whenever it declines to answer
   3. Because a fixed string costs fewer tokens than a generated apology
   4. Because the grounding checker cannot score sentences without citations
   Show the answer

   A. Because a fixed string can be detected and counted in evaluation; a free-form apology cannot
   The exact text addresses the generator answering from its weights: abstentions become measurable, and refusal cases can be counted exactly.

2. What does the chapter say fine-tuning does more reliably than adding facts?

   1. It removes the need for an evaluation set
   2. It makes the model's citations traceable to a clause
   3. It changes behaviour, such as tone, format and vocabulary
   4. It moves the organisation from provider to deployer of the model
   Show the answer

   C. It changes behaviour, such as tone, format and vocabulary
   Fine-tuning changes behaviour more reliably than it adds facts, and facts in weights go stale, which is one reason retrieval is usually preferred for knowledge.

3. Which remedy does the chapter give for the failure in which the system leaks a passage the caller is not entitled to read?

   1. A re-ranking stage after retrieval
   2. Hybrid lexical and semantic retrieval with query rewriting
   3. A larger context window for the generator
   4. The entitlement filter before similarity search
   Show the answer

   D. The entitlement filter before similarity search
   Entitlements are applied before search so that no restricted chunk is ever a candidate; for Nordvik Region, retrieving internal guidance for a citizen would be a disclosure.