Large Language Models and Retrieval-Augmented Generation

1. Introduction

Large language models generate likely text from learned patterns; fluent output is not proof of correctness. Retrieval-augmented generation adds relevant source passages to a request so answers can be grounded in a controlled knowledge collection.

2. Core concepts

An indexing pipeline extracts text, chunks it, stores metadata, and creates embeddings. At query time, the application embeds the question, retrieves candidates, enforces access rules, and supplies selected context to a model. Chunk size, ranking, and source permissions influence answer quality and safety.

3. Topics covered in this module

• Language-model history, tokens, context windows, generation, and limitations
• Embeddings, chunking, vector similarity, metadata, and retrieval
• RAG prompts, source citations, evidence sufficiency, and evaluation
• Prompt injection, access filtering, secret handling, and model output validation

4. Syntax and how to read it

query = 'How long are reports retained?'
hits = store.search(query, filters={'team': current_team}, top_k=4)
context = '\n'.join(hit.text for hit in hits)

The access filter is applied during retrieval before text enters the model context. A production implementation should enforce identity and permissions in trusted application code.

5. Practical workflow

Index approved documents with source and access metadata, retrieve only records the current user may see, inspect evidence quality, then generate an answer with citations. Evaluate retrieval and answer correctness separately.

6. Common mistakes

• Retrieving unauthorized documents and relying on the model prompt to enforce access.
• Assuming similar embeddings guarantee that retrieved text supports the final answer.

7. Best practices

• Enforce document permissions before adding any text to model context.
• Return traceable citations and say when evidence is insufficient.

8. Practice task

Why should retrieval quality be evaluated separately from generated-answer quality?

9. One possible solution

A correct answer can come from model memory despite poor retrieval, or excellent evidence can be misread by generation. Measuring each stage locates the failure and guides fixes.

10. Summary

Understand language-model limits and ground answers with permission-aware document retrieval. Enforce document permissions before adding any text to model context. Return traceable citations and say when evidence is insufficient.

11. What comes next

The next module, Leveraging AI as a Data Scientist, builds on this foundation and applies it to a new part of the analytics workflow.