# (Day 6/10) Context Windows & Retrieval: Feeding Models the Right Info
## Understanding Context Windows
**Definition:** A context window represents the amount of text an AI model can process simultaneously—essentially its working memory, measured in tokens.
**Evolution:** - 2022-2023: GPT-3.5 featured 4,096 tokens - 2024: Models reached 32,000-128,000 tokens - 2025: Leading models offer 128,000 to 2 million tokens (e.g., Gemini processes roughly 3,000 pages)
**Advantages of Larger Windows:** - Improved recall and information retention - Complete document processing - Integration of fresh data - Enhanced developer productivity
**Limitations:** - Higher computational costs and inference speed reductions - Reduced transparency and explainability - Diminishing returns from information overload - Memory management challenges
## Retrieval-Augmented Generation (RAG)
**Definition:** RAG enables generative AI models to retrieve and incorporate new information, modifying how LLMs respond to queries about specified document sets.
**RAG Process Steps:** 1. Data Processing (converting external information to vector embeddings) 2. Storage in vector databases 3. Query Processing (converting user queries to vectors) 4. Retrieval (matching queries with stored embeddings) 5. Generation (combining retrieved information with model responses)
**Benefits:** - Access to current information beyond training data cutoffs - Reduced hallucinations - Domain-specific customization - Cost-effective alternative to fine-tuning
Author: Dr. Hernani Costa — Founder of First AI Movers and Core Ventures. AI Architect, Strategic Advisor, and Fractional CTO helping Top Worldwide Innovation Companies navigate AI Innovations. PhD in Computational Linguistics, 25+ years in technology.
Originally published at First AI Movers under CC BY 4.0.