Master AI & LLM Context: From Basics to Production
The best zero-jargon starting point. IBM explains context windows clearly without vendor bias — what they are, why they matter, and how they limit what an LLM can "remember" in a conversation. Read this first if you're new to the topic.
Goes beyond theory into practical implications for developers — how tokens are counted, what happens when you hit the limit, and how context window size affects real document-processing pipelines. Great second read after the basics.
The authoritative reference if you're building with Claude. Covers exact window sizes per model, how to count tokens programmatically, and best practices for staying within limits. Bookmark this — you'll return to it constantly.
Straight from the team that builds Claude. This post explains how to structure context so agents behave reliably — what to include, what to omit, and how to avoid common failure modes. Essential reading for anyone building multi-step AI pipelines.
The most comprehensive academic overview of the field as of 2025. Covers context compression, retrieval, memory management, and structured context techniques across dozens of papers. Use it as a map of the entire landscape.
Deep-dives into how researchers push models beyond their training context limit — RoPE scaling, ALiBi, sliding window attention, and more. Invaluable if you want to understand why some models handle long documents better than others.
The most hands-on production guide in this stack. Covers memory hierarchies (short-term, long-term, episodic), context curation strategies, and real tooling choices. If you're shipping an AI agent in 2025, start here for the engineering side.
A community-maintained cheat sheet of context window and token limits for every major model — OpenAI, Anthropic, Google, Meta, Mistral, and more. Saves you hunting through multiple provider docs when you need to pick the right model for a long-context task.
A clean, accessible explainer that walks through tokens, attention mechanisms, and why window size directly impacts what an LLM can reason about. Good for sharing with non-technical teammates who need to understand context limits.
Practical tips for getting the most out of Claude's large context window — how to structure long prompts, where to place key information, and what parameters to tune. Immediately applicable to anyone prompting Claude in production.
A real-world case study in context engineering applied to observability data. Shows concrete patterns — dynamic retrieval, context scoping, compression — that translate well to any domain where you're feeding large data to an LLM.
Packed with actionable techniques to reduce token usage without hurting output quality — prompt trimming, summarization strategies, and conversation pruning. Useful for anyone watching costs or hitting context limits in long sessions.
The most thorough 2025 guide to Retrieval-Augmented Generation — the primary technique for giving LLMs access to knowledge beyond their context window. Covers chunking, vector retrieval, reranking, and advanced variants like GraphRAG and HyDE.
The research paper that introduced parallel context processing as a way to overcome fixed-length limits. A foundational read if you want to understand how modern long-context architectures evolved — and why some models handle multi-document tasks so well.
⚠️ Published 2024 — compares GPT-4o, Gemini 1.5 Pro, and Claude 3 Opus, which have since been superseded. The architectural insights and context-length trade-off analysis remain valid; just know the specific model specs are no longer current. Good reference for understanding how to evaluate models on context tasks.