90% of AI Users Are Getting Mediocre Output. Don't Be One of Them (Stop Prompting, Do THIS Instead)

How to Customize AI for Better Results

Understanding the Limitations of Default AI Models

• The speaker reveals that default versions of AI models like ChatGPT, Claude, and Gemini do not yield exceptional results without customization.

• Emphasizes the importance of understanding various levers available in these models to enhance their performance.

• Introduces four key levers: memory, instructions, style controls, and apps/tools that can significantly improve user experience.

The Concept of Averaging in AI Responses

• Discusses how AI tends to average responses to cater to a broad audience rather than individual needs.

• Uses the analogy of a restaurant creating a generic pizza dish that satisfies most customers but fails to delight any specific one.

• Explains that this averaging leads to responses that are technically correct but lack personalization or depth relevant to individual users.

Mechanism Behind Average Responses

• Describes how when asking for recommendations or advice, users receive generalized answers aimed at satisfying the majority rather than tailored solutions.

• Highlights the disconnect between what users expect from AI and what they actually receive due to this averaging process.

Reinforcement Learning and Its Impact on Output Quality

• Details how modern AIs learn through reinforcement learning from human feedback where outputs are rated by human raiders who may not understand specific user contexts.

• Points out that raiders evaluate responses based on general helpfulness rather than personal relevance, leading models toward median outputs.

Implications of Using Default Settings

• Argues that using default settings means receiving answers optimized for an average person instead of personalized insights tailored for individual needs.

• Suggests that while prompting was previously the only way to escape mediocrity in responses, new methods have emerged for better customization.

Leveraging Memory as a Customization Tool

• Introduces memory as a crucial lever allowing AI systems to retain information about users across conversations, enhancing context awareness.

AI Memory and Instruction Mechanisms

Understanding ChatGPT's Memory System

• ChatGPT utilizes a multi-layered memory system, including saved memories for specific facts and a broader chat history to understand user preferences.

• The AI can reference past conversations with clickable citations, enhancing transparency but still lacking in comprehensive memory implementation.

• Project-specific memory allows users to isolate discussions within projects, ensuring that context remains relevant and focused.

• Users are encouraged to instruct ChatGPT on specific preferences for better personalization, such as preferred response lengths or audience considerations.

Comparing Claude's Memory Functionality

• Claude operates differently by using a retrieval mechanism for past conversations and generating periodic memory summaries of key facts.

• Each project in Claude has its own isolated memory space, preventing overlap between different contexts like work and personal planning.

• Claude supports limited interoperability for importing/exporting memories from other platforms like ChatGPT, though not seamlessly.

Leveraging Gemini's Personal Intelligence

• Gemini connects with Google apps (Gmail, YouTube), allowing it to pull contextual information directly from user data for personalized responses.

• Users must weigh the trade-off between privacy and personalization when deciding how much data to share with Google services.

Utilizing Instructions Effectively

ChatGPT's Instruction Layers

• ChatGPT features multiple instruction layers including custom instructions that guide how it should respond based on user input.

• Specificity is crucial; clear directives enhance the model’s understanding of desired behaviors in various contexts.

Claude's Instruction Features

• Claude offers distinct style features where users can upload writing samples to help the AI match their tone more effectively than verbal descriptions alone.

Importance of Documentation in Development

Using Claude Markdown Files

• Developers utilizing Claude Code maintain a living document (Claude markdown file), which evolves over time as rules are added based on performance feedback.

Tools and Capabilities Overview

Configuring Apps and Tools

• The AI’s capabilities include web searching, running code, creating files, etc., which can be configured differently depending on user needs.

How AI Systems Connect to External Tools

Understanding the MCP Standard

• The MCP (Multi-Connector Protocol) standard allows AI systems to connect with external tools seamlessly, similar to how USB-C functions for devices.

• Over 10,000 MCP servers are currently operational, indicating widespread adoption and future growth in this area.

Usage of Connectors in AI

• ChatGPT refers to these connectors as apps, enabling connections to services like Gmail and Calendar automatically when relevant. However, the term "relevant" can be ambiguous in practice.

• Users may need to remind ChatGPT about these connections due to its limited search capabilities.

Connectivity Challenges Across Platforms

• Claude offers a broader range of MCP servers but faces reliability issues; connecting to some services like Stripe can be challenging while others like Figma are easier.

• Asana has recently been added as a connector for Claude, while Gemini's tool integration is considered lacking compared to competitors like ChatGPT and Claude.

Leveraging Tools Effectively

• Users should consider their toolsets intentionally and regularly check for available MCP connectors that enhance their workflow with AI systems. This approach ensures that tools significantly influence input rather than merely being additional features.

Style and Tone Control in AI Interactions

• ChatGPT provides eight personality presets ranging from friendly to cynical, along with granular controls over characteristics such as warmth and enthusiasm. Users should clearly define their desired style without conflicting instructions to optimize performance.

• Claude offers three built-in styles: formal, concise, and explanatory; users are encouraged to select styles that reflect their actual behavior rather than aspirational preferences for better interaction outcomes.

Importance of Specific Instructions

• Vague instructions often lead to suboptimal responses from AI models; specific guidance enhances output quality significantly by providing context about user needs or expertise levels. For example, asking for diagnostic questions yields better learning experiences than simply requesting help.

Capturing Corrections for Improved Performance

• Successful users capture corrections made during interactions with AI systems and encode them back into the model’s instructions or memory settings for continuous improvement over time. This disciplined approach leads to compounding benefits in productivity and accuracy of responses from the AI system.

Limitations of Personalization

• While steering improves personalization within models, it does not resolve all issues such as hallucinations or creative limitations inherent in generative tasks due to training data biases toward average outputs—effort is required for effective steering over time despite potential frustrations encountered along the way.

Understanding AI Utilization

The Value of Regular AI Use

• Using AI multiple times a week can significantly enhance output quality, making the initial investment worthwhile due to compounding time savings.

• Start with one specific task where AI output feels inadequate; track adjustments over sessions and implement custom instructions for improved results.

• Default outputs from AI are designed for average users, which may not meet individual needs; personal constraints and goals should guide usage.

• Users have various levers beyond prompting—such as memory, instructions, tools, and style—that can be utilized to tailor the AI experience effectively.