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.
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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.
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.
When an AI's context window is nearly full, don't rely on its automatic compaction feature. Instead, proactively instruct the AI to summarize the current project state into a "process notes" file, then clear the context and have it read the summary to avoid losing key details.
AI data agents can misinterpret results from large tables due to context window limits. The solution is twofold: instruct the AI to use query limits (e.g., `LIMIT 1000`), and crucially, remind it in subsequent prompts that the data it is analyzing is only a sample, not the complete dataset.
Even models with million-token context windows suffer from "context rot" when overloaded with information. Performance degrades as the model struggles to find the signal in the noise. Effective context engineering requires precision, packing the window with only the exact data needed.
The most effective way to use AI is not for initial research but for synthesis. After you've gathered and vetted high-quality sources, feed them to an AI to identify common themes, find gaps, and pinpoint outliers. This dramatically speeds up analysis without sacrificing quality.
Contrary to intuition, providing AI with excessive or irrelevant information confuses it and diminishes the quality of its output. This phenomenon, called 'context rot,' means users must provide clean, concise, and highly relevant data to get the best results, rather than simply dumping everything in.
Long conversations degrade LLM performance as attention gets clogged with irrelevant details. An expert workflow is to stop, ask the model to summarize the key points of the discussion, and then start a fresh chat with that summary as the initial prompt. This keeps the context clean and the model on track.
When an AI tool automatically gathers rich, timely context from external sources, user prompts can be remarkably short and simple. The tool handles the heavy lifting of providing background information, allowing the user to make direct, concise requests without extensive prompt engineering.
Instead of a single massive prompt, first feed the AI a "context-only" prompt with background information and instruct it not to analyze. Then, provide a second prompt with the analysis task. This two-step process helps the LLM focus and yields more thorough results.
