This Workflow Will Change How You Use AI Forever

How to Create Efficient AI Workflows

Introduction to the Problem

• The speaker discusses their experience creating multiple GPTs over two days to solve a problem related to generating PDFs efficiently.

• Corey Mlan introduces himself and emphasizes the importance of using AI as reliable infrastructure rather than starting from scratch each time.

Key Concepts in Using AI Effectively

• Mlan highlights that relying on new prompts for every interaction with AI wastes time, as it requires repetitive explanations of business challenges.

• He notes that this repetitive process accumulates over time, leading to significant productivity loss when working with AI tools.

Time Management and Productivity

• The speaker describes how users often feel like they are babysitting an intern (AI), which detracts from actual productivity.

• By implementing a standardized workflow after solving problems, users can save substantial amounts of time—up to 10 hours per week.

Standardizing Processes

• Mlan encourages viewers to transform conversations with AI into standardized workflows by mapping out successful and unsuccessful steps taken during problem-solving.

• He shares a personal example of designing professional-looking PDFs, illustrating how he turned his learning process into a repeatable system.

Practical Steps for Implementation

• Instead of asking ChatGPT for content creation directly, Mlan suggests analyzing your own writing process and documenting it systematically.

• He recommends using voice memos for brainstorming ideas without worrying about structure initially; this allows for free-flowing thought processes.

Transitioning from User to Architect

• After recording thoughts, users can copy transcripts and let AI organize them into coherent processes, enhancing efficiency significantly.

• Mlan critiques current limitations in platforms like ChatGPT compared to others that offer more lateral features for creativity and productivity.

Understanding AI Control and Router Prompts

The Challenge of AI Control

• Many users feel the need to constantly prompt AI, fearing that without guidance, it may "hallucinate" or deviate from intended tasks.

• These concerns are valid; thus, introducing the concept of a "router prompt" is essential for effective AI management.

What is a Router Prompt?

• A router prompt is described as one of the most crucial prompts to write, serving as the foundational control mechanism for AI behavior.

• It contains step-by-step logic that governs how the AI operates within defined constraints based on platform limitations (e.g., character limits in ChatGPT).

Components of a Router Prompt

Governance Layer

• This layer acts as a constitution or rule book, outlining boundaries and expected behaviors for the AI.

• Writing this in deterministic language ensures that rules are perceived as immutable by the AI.

Registry

• The registry component links to files hosted on servers or lists uploaded documents, providing context for tasks assigned to the model.

Conditional Logic

• Incorporating conditional logic allows for dynamic decision-making processes within tasks (e.g., writing blog posts).

• The speaker emphasizes reviewing decisions line by line and recording voice memos to enhance clarity in instructions.

Structuring Instructions with Conditional Logic

• Detailed notes are included in instructions so that the system can respond appropriately at various decision points.

• An alpha-numerical system is used to organize steps clearly, ensuring comprehensive coverage of all potential scenarios.

Importance of a Library for High Quality Output

• A well-defined library containing necessary resources (files, PDFs, frameworks) enhances an AI's ability to produce quality outputs consistently.

• For specialized workflows (like accounting), specific formulas should be included in this library to guide accurate task execution.

Understanding AI Model Efficiency

Importance of Clear Instructions

• The effectiveness of AI models increases when users provide clear and specific instructions, reducing the likelihood of hallucinations.

• Users should verify outputs, especially if they rely on the model for repeated tasks; confidence in results grows with well-defined parameters.

Components of a Healthy Library

Synthetic Data

• Synthetic data is crucial as it mimics real data to enhance AI's expertise without using actual data.

Artifact Templates

• Creating templates for frequently used documents (e.g., proposals) allows the model to generate consistent outputs by referencing these templates during task execution.

Prompts for Specific Tasks

• Custom prompts can be tailored for specific stages in workflows, such as title writing, ensuring that each prompt focuses on a particular task effectively.

Workflow Optimization with Router Prompts

• Instead of combining multiple prompts into one, breaking them down into smaller tasks allows for more specialized and effective responses from the AI.

• A router prompt orchestrates the sequence of smaller prompts, enhancing workflow efficiency by managing task execution order.

Building an Effective System

• The router prompt should be developed last after establishing all components (synthetic data, templates, and individual prompts), providing a comprehensive view necessary for effective operation.

• This structured approach saves time and effort by creating standardized workflows that can be reused consistently across similar tasks.

Leveraging AI for Repetitive Tasks

• Users are encouraged to think about their regular interactions with AI as opportunities to create reusable tools or assets that streamline future processes.

• If faced with challenges while using AI, consider whether the task is repetitive; if so, revisit strategies to build efficient systems rather than seeking immediate solutions.