Use a dedicated tool like Manus for initial research. It runs independently and provides traceable sources, allowing you to vet information before feeding it into your core OS (like Claude). This prevents your AI's memory from being 'polluted' with unverified or irrelevant data that could skew future results.
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To get highly specialized AI outputs, use ChatGPT's "projects" feature to create separate folders for each business initiative (e.g., ad campaign, investment analysis). Uploading all relevant documents ensures every chat builds upon a compounding base of context, making responses progressively more accurate for that specific task.
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.
To maximize an AI assistant's effectiveness, pair it with a persistent knowledge store like Obsidian. By feeding past research outputs back into Claude as markdown files, the user creates a virtuous cycle of compounding knowledge, allowing the AI to reference and build upon previous conclusions for new tasks.
Before writing any code for a complex feature or bug fix, delegate the initial discovery phase to an AI. Task it with researching the current state of the codebase to understand existing logic and potential challenges. This front-loads research and leads to a more informed, efficient approach.
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.
Unlike other LLMs that handle one deep research task at a time, Manus can run multiple searches in parallel. This allows a user to, for example, generate detailed reports on numerous distinct topics simultaneously, making it incredibly efficient for large-scale analysis.
Use the Claude chat application for deep research on technical architecture and best practices *before* coding. It can research topics for over 10 minutes, providing a well-summarized plan that you can then feed into a dedicated coding tool like Cursor or Claude Code for implementation.
To create a reliable AI persona, use a two-step process. First, use a constrained tool like Google's NotebookLM, which only uses provided source documents, to distill research into a core prompt. Then, use that fact-based prompt in a general-purpose LLM like ChatGPT to build the final interactive persona.
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.
To combat generic AI content, load your raw original research data into a private AI model like a custom GPT. This transforms the AI from a general writer into a proprietary research partner that can instantly surface relevant stats, quotes, and data points to support any new piece of content you create.
