What You Will Learn

• Distinguish reasoning models (planners) from standard models
• Write clear, simple prompts and structure inputs with delimiters
• Avoid Chain-of-Thought prompts and use zero-shot then few-shot
• Create developer messages and enable API output formatting
• Apply models to multi-step planning, data parsing, visual reasoning, and code generation

Study Guide

Introduction

Welcome to the comprehensive video course: How to Use Reasoning Models? In today's ever-evolving technological landscape, understanding and effectively utilizing reasoning models is not just an advantage—it's a necessity. This course is designed to take you from the basics to advanced applications of reasoning models, providing you with the tools and knowledge to harness their full potential. Whether you're new to AI or looking to refine your skills, this guide will help you navigate the complexities of reasoning models, focusing on practical applications and best practices. By the end of this course, you'll be well-equipped to apply these models thoughtfully and effectively in various scenarios.

Distinguishing Reasoning Models

Reasoning models, often referred to as "planners," are a step beyond standard AI models, known as "workhorses." While workhorse models are optimized for efficiency and speed in handling simpler tasks, reasoning models excel in areas requiring deeper analysis and strategic thinking. They are particularly adept at:

• Handling ambiguity and complex, unstructured data.
• Identifying relationships and patterns within data.
• Executing multi-step planning and strategic tasks.

For example, consider a scenario where a company needs to analyze customer feedback to improve its products. A reasoning model can sift through large volumes of unstructured feedback, identify common themes, and suggest actionable improvements. Similarly, in financial forecasting, reasoning models can process complex datasets to identify trends and predict future market movements.

Effective Prompting Techniques

Prompting reasoning models effectively is crucial to achieving optimal results. Here are some key strategies:

Keep Prompts Simple and Direct

When crafting prompts, avoid unnecessary jargon or complex phrasing. The mantra here is: "keep your prompt simple and to the point no extra fluff no unnecessary words just clear direct instructions."
For instance, if you want the model to generate a market analysis report, a simple prompt like "Analyze the current market trends in the tech industry and provide a summary" is more effective than a convoluted request.

Avoid Chain of Thought (CoT) Prompting

Unlike standard models, reasoning models do not require explicit "think step by step" instructions. As reasoning models are inherently designed to strategize, adding such instructions can be redundant. For example, instead of instructing the model to "consider each step carefully," simply provide the task and let the model's internal reasoning capabilities do the work.

Utilize Delimiters

Structure your prompts using separators like markdown, XML tags, or section titles. This approach "allows the reasoning model to process the information in a more structured way helping it think better and generate more accurate outputs."
For example, when asking the model to create a project plan, you can use:

Context: Background of the project
Example: A sample project plan
Instruction: Create a detailed project plan with timelines and resources

Try Zero-Shot First, Then Few-Shot if Needed

Leverage the inherent reasoning capabilities of these models by initially providing only instructions. If the output is unsatisfactory, then introduce relevant examples ("few-shot") that closely align with the desired format and instructions. For instance, if you need a model to write a product description, start with a simple prompt. If the output doesn't meet expectations, provide a few examples of well-written descriptions to guide the model.

Be Specific and Explicit

When setting conditions (e.g., a budget), clearly define the parameters and, importantly, state the goal of the prompt. This "helps guide its thinking and iterative process allowing for more focused and relevant responses."
For instance, if you're using the model for budget planning, specify the total budget and key spending areas to ensure the model's output aligns with your financial goals.

Developer Messages (API)

For API users, "developer messages are replacing system messages" as a way to guide the model. To enable markdown formatting in API responses (like separated code blocks), include the string "formatting re-enabled" on the first line of the developer message. This ensures that the output maintains the desired structure and readability.

Example Prompt Structure

The video course highlights an example prompt structure credited to Alvaro Cintas:

• Context: Provide background information to aid the model's understanding.
• Example: Include relevant examples demonstrating the desired style, tone, and structure. This "helps guide the model."
• Instruction: Clearly state the task and any limitations or requirements (e.g., "original, engaging, and suitable for [target audience]"). This section should also specify the "goal of this prompt to ensure that the generated content matches the style tone and structure of the provided examples."

For instance, if you're using the model to draft a marketing email, you might structure your prompt like this:

Context: Background on the product being promoted
Example: A sample of a successful marketing email
Instruction: Write an engaging email promoting the product, highlighting its key features and benefits

Use Cases for Reasoning Models

Reasoning models are particularly powerful in a variety of complex scenarios:

Navigating Ambiguous Tasks

Reasoning models will actively seek clarification when the prompt's meaning is unclear, "ensuring they understand your intent before proceeding."
For example, if tasked with organizing a company's data into meaningful categories, the model might ask clarifying questions to ensure it understands the criteria for categorization.

Parsing Large Amounts of Data

These models can "sift through massive amounts of unstructured information and extract the most relevant details," likened to "finding a needle in a haystack."
Consider a scenario where a legal firm needs to review thousands of documents for a case. A reasoning model can efficiently identify and summarize key information, saving time and reducing manual effort.

Identifying Relationships in Data

Their reasoning capabilities allow them to "organize information correctly and create structured profiles" by identifying connections within unstructured data.
For example, in a healthcare setting, a reasoning model can analyze patient data to identify correlations between symptoms and potential diagnoses.

Multi-Step Agentic Planning

These models can "lay out a complete plan from start to finish" and delegate tasks to different language models based on computational needs.
In project management, a reasoning model can create a comprehensive project timeline, assign tasks, and monitor progress, adjusting the plan as needed.

Visual Reasoning

Models like 01, combining vision and reasoning, can analyze complex visuals such as architectural drawings to "generate a comprehensive bill of materials."
For instance, an architecture firm could use a reasoning model to analyze blueprints and automatically generate a list of required materials and costs.

Coding

While not necessarily faster than non-reasoning models (with Claude being an exception among non-reasoning models), reasoning models tend to make "far fewer mistakes," reducing the need for extensive debugging.
For example, a software development team can use reasoning models to write code snippets, ensuring accuracy and reducing the time spent on error correction.

Developer Messages vs. System Messages

The video course clarifies that in the OpenAI API, "developer messages are replacing system messages." These serve the same purpose of guiding the model's behavior. For instance, if you're using the API to generate a structured report, a developer message can specify the format and key elements to include, ensuring consistency in the output.

Conclusion

In conclusion, reasoning models represent a significant advancement in AI technology, offering enhanced capabilities for complex tasks requiring deep analysis and strategic thinking. By focusing on clarity, structure, and understanding the inherent capabilities of these models, users can achieve more accurate and relevant outputs. The strategies and examples provided in this course equip you with the knowledge to effectively leverage reasoning models across various applications. As you apply these skills, remember that thoughtful and strategic use of reasoning models can lead to innovative solutions and improved decision-making in your professional endeavors.

Podcast

Frequently Asked Questions

Welcome to the FAQ section for the 'Video Course: How to use reasoning models?' This resource is designed to answer your most pressing questions about reasoning models and their applications. Whether you're a beginner looking to understand the basics or a seasoned professional seeking advanced insights, this FAQ aims to provide clear, practical answers to enhance your learning experience.

What distinguishes reasoning AI models from standard AI models?

Reasoning models, unlike standard AI models (referred to as "workhorses"), are specifically designed with enhanced reasoning capabilities. This means they excel at tasks requiring logical inference, planning, and understanding context in a deeper way. While standard models might focus on pattern recognition and efficient execution, reasoning models ("planners") can navigate ambiguous instructions, process complex information to extract relevant details, and even create multi-step plans.

How does the prompting strategy differ when interacting with reasoning models compared to standard models?

When prompting reasoning models, it's crucial to be clear, concise, and avoid unnecessary "fluff". Unlike standard models, you should generally refrain from using Chain of Thought prompts (asking the model to explain its reasoning step-by-step) as reasoning models are already designed to reason internally. Instead, focus on providing direct instructions and leveraging delimiters to structure your input, separating context, examples, and instructions for better processing.

Why is it recommended to use delimiters when prompting reasoning models?

Using delimiters, such as markdown, XML tags, or section titles, helps reasoning models to process information in a more structured manner. By clearly separating different parts of your prompt (e.g., context, examples, instructions), you enable the model to better understand the relationships between them, leading to more accurate and relevant outputs. This structured approach aids the model's internal reasoning process.

What does the "try zero shot first, then few shot if needed" advice mean in the context of reasoning models?

Given their inherent reasoning capabilities, it's advisable to first attempt prompting reasoning models without providing any examples (zero-shot prompting). They are often good at predicting the desired output based solely on clear instructions and context. However, if the initial response isn't satisfactory or if you encounter edge cases, then you can introduce a few carefully selected examples (few-shot prompting) that closely align with your prompt instructions to guide the model.

How can I make my prompts more effective when using reasoning models, particularly when setting conditions?

When imposing conditions in your prompts for reasoning models, it's essential to be specific. For example, if you're setting a budget, clearly state the budget amount and any related constraints. Furthermore, always specify the goal of your prompt in relation to these conditions. Explaining the intended outcome within the given limitations helps the reasoning model to focus its iterative process and generate more targeted and relevant responses.

What are some key use cases where reasoning models demonstrate significant advantages over standard AI models?

Reasoning models excel in several key areas. They are particularly useful for navigating ambiguous tasks where clarification might be needed, sifting through large amounts of unstructured data to find specific information, identifying relationships within complex datasets, and generating structured outputs. Additionally, their ability for multi-step agentic planning, visual reasoning (understanding detailed images like architectural drawings), and producing code with fewer errors highlights their advanced capabilities.

How do reasoning models handle ambiguity in prompts?

Unlike standard models that might make assumptions when faced with unclear instructions, reasoning models are designed to ask for clarification when the meaning of a prompt is ambiguous. This proactive approach ensures they correctly understand the user's intent before proceeding, leading to improved accuracy and saving time that might otherwise be spent correcting misunderstandings.

What considerations should be made when using reasoning models via an API regarding output formatting?

When interacting with reasoning models through an API, you might notice that they avoid generating responses with markdown formatting, such as separated code blocks, by default. To enable this formatting in the API response, you need to include the string "formatting re-enabled" on the first line of the developer message. This explicitly instructs the model to include markdown formatting in its output.

What is prompt engineering and why is it important?

Prompt engineering is the process of designing and refining prompts to elicit desired and accurate responses from AI models. It's crucial because the effectiveness of an AI model heavily depends on the quality of the prompts provided. Well-crafted prompts can significantly enhance model performance, leading to more relevant and precise outputs.

How do reasoning models process large data sets?

Reasoning models excel at processing large amounts of unstructured data due to their advanced reasoning capabilities. They can efficiently sift through data, pinpoint the most relevant details, and identify relationships between different points within the data. This makes them invaluable for tasks that involve analyzing complex datasets.

Can reasoning models improve customer service operations?

Yes, reasoning models can significantly enhance customer service operations. They can be used to understand and process policy documents, create structured routines, and generate detailed responses to customer queries. By leveraging their ability to plan and reason, these models can streamline operations and improve customer satisfaction.

What are the challenges of using reasoning models in business?

While reasoning models offer numerous advantages, they also come with challenges. These include the need for clear and precise prompts, potential computational resource requirements, and the necessity for human oversight to ensure that the model's outputs align with business goals. Additionally, integrating these models into existing workflows may require significant changes to processes and systems.

How can reasoning models aid in strategic planning?

Reasoning models are adept at multi-step agentic planning, making them ideal for strategic planning tasks. They can analyze various scenarios, predict outcomes, and suggest optimal strategies based on available data. This capability allows businesses to make informed decisions and develop comprehensive plans that consider multiple variables and potential outcomes.

What is the role of specificity in prompting reasoning models?

Specificity in prompts is crucial for reasoning models. Clearly stating conditions, limitations, and goals helps guide the model's thinking and iterative process. This ensures that the generated responses are focused, relevant, and aligned with the desired outcomes, ultimately leading to more effective use of the model.

How do reasoning models handle visual information?

Some reasoning models, like 01 with vision capabilities, can understand and interpret visual information. They can analyze complex images, such as architectural drawings, and extract meaningful insights. This visual reasoning capability allows them to perform tasks like generating a bill of materials from a blueprint, showcasing their versatility beyond text-based tasks.

What are the best practices for using reasoning models effectively?

To use reasoning models effectively, follow these best practices: keep prompts simple and direct, avoid unnecessary complexity, use delimiters for structure, and start with zero-shot prompting before moving to few-shot if needed. These practices help in achieving more accurate and relevant outputs from the model.

How can reasoning models assist in code generation?

Reasoning models can aid in code generation by producing code with fewer errors, reducing the need for extensive debugging. While they may not be the fastest, their ability to understand complex instructions and generate accurate code makes them valuable for developing robust software solutions.

What is the difference between planners and workhorses in AI?

In AI, "planners" refer to reasoning models that excel at strategizing and planning, while "workhorses" are standard models focused on efficient task execution. Planners are used for tasks requiring deep reasoning, whereas workhorses handle simpler, routine tasks, emphasizing a division of labor based on reasoning and efficiency.

How do reasoning models handle edge cases?

Reasoning models are designed to manage edge cases by asking for clarification when faced with unusual or atypical scenarios. This proactive approach ensures they correctly interpret the user's intent, reducing errors and improving the accuracy of their outputs.

How can reasoning models improve decision-making in business?

Reasoning models enhance decision-making by providing data-driven insights and strategic recommendations. They can analyze complex datasets, identify patterns, and suggest optimal courses of action, enabling businesses to make informed decisions that align with their goals and objectives.

What are the benefits of using zero-shot prompting with reasoning models?

Zero-shot prompting allows reasoning models to generate outputs based on clear instructions and context without requiring examples. This approach leverages the model's inherent reasoning capabilities, often resulting in accurate and relevant responses without the need for additional guidance.

How can reasoning models be integrated into existing business processes?

Integrating reasoning models into business processes involves identifying areas where their reasoning capabilities can add value, such as strategic planning, data analysis, or customer service. It requires aligning the model's outputs with business objectives and ensuring seamless integration with existing systems and workflows.