“context-window”
Context Budgeting in Agent RAG: What to Include, What to Drop
Master context window management for agent RAG systems with strategies for token budgeting, relevance scoring, compression, and dynamic context allocation using Gemini API patterns.
Re-Ranking Retrieved Context Before Injection: Precision Over Recall in RAG
Improve RAG accuracy by re-ranking retrieved documents with cross-encoders, semantic similarity, and diversity-aware ranking before injecting into LLM context.
Optimizing Context Windows: Token Budgeting Best Practices
A 10M-token window is a trap, not a license. Learn to budget tokens like memory: account for every section, prioritize by recency and relevance, and cut cost and latency.
Context Window Engineering for Long-Running Loops
Master context drift, context rot, memory compaction, Git worktree isolation, and token budgeting for reliable long-running AI agent loops.
Gemini 3: A Deep Dive into the 10M+ Token Context Window and Infinite Memory
Exploring the revolutionary 10 million token context window of Gemini 3 and how it enables infinite memory for AI agents.