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.

Getting high-quality results from AI doesn't come from a single complex command. The key is "harness engineering"—designing structured interaction patterns between specialized agents, such as creating a workflow where an engineer agent hands off work to a separate QA agent for verification.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.

Instead of immediately asking an AI to perform a complex task, first prompt it to create a functional spec or a sequential plan. Go back and forth to align on this plan before instructing it to execute, which significantly improves the final output's quality and relevance.

For large tasks like creating a business plan, act as a project conductor. First, prompt the AI for individual components like a table of contents or specific sections. Once all parts are generated, use a final prompt to synthesize them into a coherent whole.

Long, continuous AI chat threads degrade output quality as the context window fills up, making it harder for the model to recall early details. To maintain high-quality results, treat each discrete feature or task as a new chat, ensuring the agent has a clean, focused context for each job.

To unlock the full potential of AI, don't just assign it single tasks. Instead, ask: 'If I had infinite, always-available junior talent, what is the ideal process I'd have them follow for a new ticket?' This framing helps you design more comprehensive, multi-step prompts and automations.

AI agents have limited context windows and "forget" earlier instructions. To solve this, generate PRDs (e.g., master plan, design guidelines) and a task list. Then, instruct the agent to reference these documents before every action, effectively creating a persistent, dynamic source of truth for the project.