AI models like Claude Code can experience a decline in output quality as their context window fills. It is recommended to start a new session once the context usage exceeds 50% to avoid this degradation, which can manifest as the model 'forgetting' earlier instructions.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

Providing too much raw information can confuse an AI and degrade its output. Before prompting with a large volume of text, use the AI itself to perform 'context compression.' Have it summarize the data into key facts and insights, creating a smaller, more potent context for your actual task.

Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.

The "Agent Skills" format was created by Anthropic to solve a key performance bottleneck. As capabilities were added, system prompts became too large, degrading speed and reliability. Skills use "progressive disclosure," loading only relevant information as needed, which preserves the context window for the task at hand.

Instead of forcing an AI to read lengthy raw documents, create consistently formatted summaries. This allows the agent to quickly parse and synthesize information from numerous sources without hitting context limits, dramatically improving performance for complex analysis tasks.

Long-running AI agent conversations degrade in quality as the context window fills. The best engineers combat this with "intentional compaction": they direct the agent to summarize its progress into a clean markdown file, then start a fresh session using that summary as the new, clean input. This is like rebooting the agent's short-term memory.

Instead of overloading the context window, encapsulate deep domain knowledge into "skill" files. Claude Code can then intelligently pull in this information "just-in-time" when it needs to perform a specific task, like following a complex architectural pattern.

To solve the problem of MCPs consuming excessive context, advanced AI clients like Cursor are implementing "dynamic tool calling." This uses a RAG-like approach to search for and load only the most relevant tools for a given user query, rather than pre-loading the entire available toolset.