5 Ways To Master Context For NEXT-LEVEL AI Performance

Context management is becoming the main lever for improving large language model output as models converge on similar capabilities. Instead of chasing ever-bigger prompts, the practical edge comes from feeding the right information—reliably, repeatedly, and with enough control that answers stay grounded in what matters to a specific project.

The simplest approach is copy-paste. Pull in a snippet of relevant text (or even an image) and include it directly in the model’s context, then ask for code or a task based on that material. It works quickly for one-off needs, including examples like pasting OpenAI-related material and then requesting code for “OpenAI GPT 4.1,” or doing the same with an image-based example. But copy-paste doesn’t scale well when the same context must be reused across sessions or projects.

For reuse, the workflow shifts to local, versionable context files. The recommended pattern is to create a documentation folder (for example, a “docs” directory) and store project-specific reference material as files such as “OpenAI docs.md.” Then the model can be prompted to use that file as context—often inside an IDE like Cursor—so the information is consistent and easy to update. This also supports multiple tagged files, letting teams build an indexed knowledge base upfront so new projects start with the right background material already staged.

Web search is the next step for dynamic context. Many tools integrate search directly into chat, enabling the model to fetch current information and return results with sources. The transcript highlights an example where a model searches Reuters and other outlets for major news items, then continues the workflow using the retrieved context and source links. The tradeoff is precision: leaving source selection to the model can pull in irrelevant material, and repeated searching can feel slow or redundant. Cursor’s web tool is also described as sometimes struggling, which reduces trust in the feature for frequent use.

To regain control, the transcript moves to custom MCP server tools that combine targeted search with controlled fetching. Using MCP connections such as “brave” and “fetch,” a user can steer the query (e.g., “find news on Ukraine May 15, 2025”), collect URLs from Brave, and then use Fetch to retrieve and render HTML from those pages. The retrieved content can then be summarized into a local artifact like “Ukraine.txt,” creating a repeatable pipeline from search → extraction → summarization.

The most advanced method is an MCP RAG (retrieval-augmented generation) server backed by a vector database. In the example, a “3JS vector search” MCP server indexes 3JS documentation into a vector store. When building a 3JS game and asking about “fog” or “textures,” the system queries the vector store and returns top documentation matches—such as linear vs. exponential fog examples—so the model can answer using project-relevant references without manually digging through documentation. The core message is that as LLMs become more similar, the biggest performance gains will come from dynamically and accurately retrieving relevant context from codebases, documentation, and curated knowledge stores—whether through local files, controlled web pipelines, or RAG servers.