Retrieval-Augmented Generation: Connecting Large Language Models to Private Data
You will learn how to build systems that let AI models read and answer questions using your own documents. We will cover how to turn text into numbers for searching, how to find the most relevant information, and how to combine that data with a prompt. You will be able to decide when to use this method instead of just giving the AI a long list of text.
The problem of AI memory
Explain why AI models need external data to answer questions about recent or private facts.
Key takeaways
- AI models are limited by a training data cutoff that prevents them from knowing recent or private information.
- Hallucinations occur when a model uses statistical patterns to guess an answer instead of accessing a factual database.
- Retrieval-Augmented Generation allows an AI to look up relevant documents in real-time to ground its answers in facts.
- Vector databases enable AI systems to find information based on the meaning of a query rather than just matching keywords.
Sources
Turning text into searchable numbers
Describe how text embeddings allow computers to find similar meanings between different sentences.
Key takeaways
- Embedding models turn text into lists of numbers called vectors that represent the meaning of a sentence.
- Sentences with similar meanings are placed close together in a high-dimensional mathematical space.
- Cosine similarity is a common tool used to measure the angle between vectors to determine how closely they relate.
- Modern search systems often combine dense embeddings for meaning with rerankers for higher accuracy.
Sources
- Best Embedding Models for RAG in 2026: A Comparison Guide
- SentenceTransformers Documentation — Sentence Transformers documentation
- Sentence Transformers Tutorial: Complete Guide (2026)
- sentence-transformers · PyPI
- Transformer (deep learning) - Wikipedia
- 5 Best Embedding Models for RAG: How to Choose the Right One
- Embedding (machine learning) - Wikipedia
Storing and finding information
Explain how vector databases store embeddings and retrieve the best matches for a query.
Key takeaways
- Vector databases store information as long lists of numbers that represent the semantic meaning of data.
- Approximate Nearest Neighbor algorithms like HNSW allow databases to find similar items quickly by searching through layers of a graph.
- Distance metrics such as cosine similarity and Euclidean distance determine how closely two vectors relate to each other in mathematical space.
- Hybrid search improves accuracy by combining the conceptual understanding of vectors with the precise term matching of keyword search.
Sources
- A High-performance Vector Search Engine on Disaggregated Memory
- Vector database vs. Relational database: 7 key differences
- 7 Vector Databases Compared for Enterprise RAG Applications
- What Is a Vector Database?
- Best Vector Databases in 2026: A Complete Comparison Guide
- [2505.11783v1] Efficient Vector Search on Disaggregated Memory with d-HNSW
- Top Vector Databases for Enterprise RAG (2026)
Preparing documents for the AI
Identify how to break large documents into smaller pieces so the AI can process them effectively.
Key takeaways
- Chunking prevents AI models from losing information or hitting token limits when reading large files.
- Recursive chunking is the most common production method because it balances speed with the ability to keep paragraphs together.
- Semantic chunking offers higher accuracy by cutting text based on topic changes but costs significantly more to process.
- Adding metadata like page numbers and dates to each chunk helps the AI filter out irrelevant information during a search.
Sources
Combining retrieved data with the prompt
Show how to write a prompt that forces the AI to use only the provided documents.
Key takeaways
- Use imperative language like "Answer using only the provided context" to set firm boundaries for the AI.
- Place the retrieved context blocks before the user query to ensure the model prioritizes the evidence.
- Require the model to include inline citations for every claim to anchor its reasoning to specific documents.
- Set the model temperature to zero and provide a clear fallback phrase for when the context is insufficient.
Sources
- Develop a RAG Solution on Azure - Prompt Engineering - Azure Architecture Center
- Lesson 25: Advanced Prompting for RAG
- Structuring Prompts for RAG Systems
- Prompt Engineering for RAG Pipelines: A Practical Guide
- A Practical Guide to Building Local RAG Applications with LangChain
- How to Build RAG with LangChain
The limits and future of RAG
Compare RAG to long-context models and identify common failure points in retrieval systems.
Key takeaways
- RAG is significantly cheaper and faster than long-context models for large datasets but suffers from information loss during the chunking process.
- Long-context models excel at global synthesis but face accuracy drops in the middle of the prompt and high latency for large token counts.
- Most production failures in retrieval systems stem from poor reasoning and the inability to link multiple facts rather than a lack of raw data.
- Modern AI architecture is shifting toward hybrid models that combine the precision of retrieval with the reasoning power of long-context windows.
Sources
- LLM Context in 2026: Long Context vs RAG Decision Guide
- LLM Context Window Comparison 2026 (refreshed): Ranked by Largest Usable Context, Not Advertised Maximum
- When 1M Token Windows Replace RAG
- Fundamental Failure Modes in RAG Systems
- RAG over Thinking Traces Can Improve Reasoning Tasks
- Synergizing RAG and Reasoning: A Systematic Review
- Gemini 1.5 Pro: An Ultra-Efficient, Multimodal System