Retrieval-augmented Generation

~60 min · 15 stations

Retrieval-augmented Generation is a self-paced learning path in Computer Science & AI, free to read, written at General Public / 9th Grade reading level. Across 15 structured stations, you will work through the core ideas step by step, each with a short quiz to check your understanding. By the end you will be able to identify why static language models struggle with new information; define the core function of large language models; recognize the value of connecting AI to private databases.

Conductor

The Conductor

Welcome aboard the RAG Express. We are moving from the static station of pre-trained models to the dynamic landscape of real-time data retrieval. Keep your digital tickets ready for inspection.

What you will learn

Complete each station to unlock the next.

FOUNDATION

Establishes the core vocabulary and essential context you need before going further.

• Identify why static language models struggle with new information

Station 01: The Limits of Static AI Models▶

• Define the core function of large language models

Station 02: Understanding Generative AI Basics▶

• Recognize the value of connecting AI to private databases

Station 03: Introduction to External Data▶

CORE CONCEPTS

Unpacks the ideas and principles that the subject is built on.

• Explain how vector databases store information for semantic search

Station 04: Vector Databases Explained▶

• Describe the process of inserting retrieved data into prompts

Station 05: The Prompt Engineering Loop▶

• Analyze how systems find contextually relevant information

Station 06: Semantic Similarity Search▶

• Evaluate the impact of context window sizes on RAG performance

Station 07: Contextual Windows and Limits▶

MECHANICS

Examines how things actually work — the processes, rules, and systems in action.

• Construct a basic workflow for document ingestion and retrieval

Station 08: Building the Retrieval Pipeline▶

• Optimize retrieval results using reranking techniques

Station 09: Refining Retrieval Quality▶

• Connect retrieval outputs to generative model inputs

Station 10: Integration with Language Models▶

APPLICATION

Puts knowledge to use through real-world scenarios and practical problems.

• Apply RAG principles to automate technical support responses

Station 11: RAG for Customer Support▶

• Implement RAG for precise document extraction tasks

Station 12: Legal and Financial Analysis▶

• Scale RAG solutions across large internal document sets

Station 13: Enterprise Knowledge Management▶

SYNTHESIS

Connects everything together and explores broader implications and open questions.

• Measure the effectiveness of retrieval and generation components

Station 14: Evaluating RAG Performance▶

• Predict the evolution of retrieval systems in AI

Station 15: Future Trends in RAG▶

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General Public / 9th GradeAI Generated · gemini-3.1-flash-lite