Perplexity is Killing Custom GPTs Ep. 309

Is Perplexity the Next Custom GPT Killer?

Introduction to the Discussion

• The speaker introduces the concept of using custom GPTs as an efficiency tool for businesses, emphasizing their importance in understanding and building automation solutions.

• The date is October 10, 2024, and the discussion will focus on whether Perplexity could potentially replace custom GPTs. Participants include Brian, Jimmy, Andy, and Beth.

Current Capabilities of Perplexity's API

• The conversation aims to explore how Perplexity's API can be integrated into automation systems like Make or Zapier.

• A comparison is drawn between custom GPT capabilities and those of Perplexity in terms of building effective automation solutions.

Benefits of Custom GPTs

• Custom GPTs have been available for over a year; they are user-friendly and allow quick problem-solving during discussions.

• OpenAI has simplified the process of creating custom GPTs, making them accessible even for non-experts.

• Previous tools like Respell were slower compared to the rapid deployment capabilities offered by custom GPT technology.

Live Search Functionality

• Custom GPTs excel in providing live search results that enhance automation processes significantly.

• Examples from sales prospecting illustrate how real-time information is crucial for effective lead generation and preparation for discovery calls.

Integration with Business Needs

• Businesses require up-to-date information due to constant changes in industries; this need drives the effectiveness of custom GPT solutions.

• The combination of live data retrieval, document uploads, and language model integration makes custom GPT a powerful tool for communication tasks.

User Experiences with Custom GPT Development

• Users share experiences about building their own custom GPT solutions easily due to their intuitive design.

Understanding the Benefits of Custom GPTs

The Need for Specific Tools

• The speaker emphasizes the importance of having specific tools tailored to their needs, expressing frustration over needing to retrain systems for repetitive tasks.

• They highlight using a summary tool daily to process transcripts, indicating that automation significantly enhances efficiency in summarizing and extracting relevant hashtags.

Exploring Powerful GPT Options

• Discussion on various powerful GPT models available publicly, with "Grimoire" mentioned as an effective example for niche use cases.

• The speaker shares experiences working on a project where they utilized a custom GPT aligned with their tech stack, facilitating deeper problem-solving compared to starting from scratch.

Evaluating Alternatives and Use Cases

• Acknowledgment that while current tools are beneficial, they are not perfect; there is a need to consider alternatives like Perplexity for certain applications.

• The conversation aims to educate listeners on reasons to either adopt or step away from specific tools based on their unique use cases.

Custom GPT Applications in Business

• Carl discusses leveraging custom GPTs for both personal and client projects, emphasizing building co-pilots using company-specific knowledge sources.

• He explains how these custom solutions can address specific business needs such as proposal writing and enhancing search capabilities beyond standard offerings.

Integration and Functionality Enhancements

• Mention of integrating additional functionalities into custom GPT setups, allowing connections with platforms like Google Docs or Teams.

• If limitations arise within the custom setup, alternative APIs can be employed to enhance digital assistant offerings without compromising functionality.

User Experience with Custom GPT Development

• Andy shares his experience building multiple custom GPT applications for personal tasks and nonprofit work, highlighting ease of use in OpenAI's interface.

Custom GPTs: A Fast and Efficient Solution?

Ease of Use and Customization

• Custom GPTs can be tailored easily, making them a quick solution for desired functionalities that are both repeatable and shareable.

• Integrating web search APIs with language models (like Perplexity) allows for building applications that automate tasks effectively.

Cost Efficiency

• Building multiple custom GPTs is cost-effective; companies can create numerous instances at a low monthly fee, enhancing efficiency across teams.

• For organizations using several custom GPTs, the potential for significant efficiency gains exists, especially when automating repetitive tasks.

Performance and Speed

• Custom GPT setups are fast to implement, aligning with an AI-first mentality where users consider AI solutions for various problems.

• The latency in obtaining results has improved significantly, providing quick responses from custom GPT configurations.

User Experience Challenges

• While the integration of different systems provides seamless user experiences, inconsistencies may arise due to individual memory variations among users.

• Achieving a consistent high-quality user experience can be challenging as different users may have varied interactions with the same custom GPT due to its predictive nature.

Limitations of Customization

• Despite their advantages, complex customizations may lead to unpredictable behaviors in how the model responds to queries.

Understanding the Complexity of Custom GPTs

The Challenge of Controlling Creativity and Temperature

• The speaker discusses the difficulty in controlling parameters like temperature and creativity within custom GPT models, emphasizing that certain prompts require different levels of creativity.

• They highlight the complexity involved when trying to dictate specific creative outputs for various parts of a task, such as writing an email versus retrieving factual information.

Utilizing Perplexity API for Enhanced Prompting

• The speaker suggests that breaking down tasks can simplify prompting, making it easier to manage complex requests.

• A simple example is provided where data from Google Sheets is researched using Perplexity, demonstrating how multiple documents can be integrated into a cohesive output.

Balancing Speed and Quality

• There’s a trade-off between speed (latency) and the quality of responses; while latency may increase with more complex setups, reliability and repeatability improve.

• Users must decide whether they prioritize low latency or consistent quality in their outputs.

Flexibility in AI Model Integration

• The discussion emphasizes flexibility in integrating new models into existing workflows without needing to recreate custom GPT setups entirely.

• This adaptability allows users to enhance functionality easily by plugging in better-performing models as they become available.

Limitations of Google Search APIs

• The speaker notes that real-time web search capabilities are crucial for certain AI applications but points out Google's lack of a comprehensive search API for raw results.

• They mention that while Google offers some APIs, they are limited primarily to custom searches on individual websites rather than broader web searches.

Use Case Dependency and Expectations

• The effectiveness of tools like Perplexity depends heavily on specific use cases; different expectations lead to varying outcomes based on precision needs.

Understanding the Challenges of AI Implementation

The Limitations of Current Systems

• Companies often rely on external teams to implement and scale their systems, which can create bottlenecks since only a select group can make necessary adjustments.

• There is a frustration when internal teams lack the ability to independently modify tools, leading to delays in implementing changes that could be easily executed if they had the capability.

Evolving Automation Needs

• The emergence of new technologies like GPT has rendered some previously built automations obsolete, prompting curiosity about future automation developments.

• While APIs like Perplexity exist for web searches, their slow response times can hinder client satisfaction compared to faster alternatives.

Individualized Use Cases in AI

• The use of AI tools tends to be highly personalized; what works for one individual or company may not suit another due to unique needs and expectations.

• Custom solutions are often required as even minor differences in organizational structure or goals can significantly impact effectiveness.

Setting Expectations with AI Tools

• Establishing clear expectations is crucial when deploying AI solutions, especially when transitioning from traditional tools where results are more predictable.

• Many individuals find themselves unexpectedly designated as "AI experts" within their organizations, leading to increased demand for custom solutions without prior experience.

Consistency vs. Predictability in Results

• Unlike traditional software (e.g., Excel), where users can expect consistent results after initial setup, AI outputs can vary widely based on user interaction and input quality.

• Building trust in AI requires demonstrating its capabilities repeatedly, as users may encounter unexpected failures despite prior successes.

Navigating User Experience Challenges

• Training users on how to effectively utilize custom-built GPT models involves hands-on demonstrations due to the variability of outcomes based on user inputs.

Understanding Variability in AI Responses

The Challenge of Consistency in AI Outputs

• The speaker discusses the inconsistency in quality of responses from AI systems, highlighting that even coworkers nearby can yield different results.

• A question is raised about user expectations regarding speed and consistency in AI outputs, acknowledging inherent variability due to the nature of AI.

• Emphasizes that while speed may be crucial for urgent tasks, the depth of research often allows for longer processing times without significant concern.

User Experience and Quality Control

• Despite variability, users generally find responses satisfactory; however, there are instances where clients have complained about poor quality.

• The speaker shares experiences with custom GPT models and emphasizes the importance of maintaining a great user experience by quickly addressing any deviations from expected performance.

Managing Deviations in Task Execution

• Custom GPT models require careful monitoring; if they deviate early on (e.g., during initial tasks), it’s advisable to restart to ensure accuracy.

• Users may not always understand how to navigate these systems effectively, leading to unexpected outcomes that need correction.

Importance of Structured Instructions

• Early task execution is critical; if initial inputs are slightly off, subsequent outputs can diverge significantly from intended results.

• The speaker describes the challenge of balancing flexibility with structure in custom GPT designs, noting that deviations can lead to unsatisfactory answers.

Sequence Dependency in Tasks

• Highlights how each task's output relies on previous results; inaccuracies compound over time if not addressed promptly.

• Discusses the necessity for clear instructions within custom GPT frameworks to ensure reliable execution across multiple tasks.

Conclusion on Customization and User Training

Understanding Custom GPTs and Their Applications

The Challenge of Switching Contexts

• Users may struggle to switch back to custom GPT settings after interacting with a general chat interface, leading to potential confusion in responses.

• Current instructions for custom GPTs lack sufficient reasoning capabilities, which can hinder effective problem-solving.

Exploring Model Variations

• There is curiosity about the impact of integrating new models (like 01) on response accuracy and workflow efficiency.

• The introduction of model selection could necessitate significant adjustments in existing workflows due to increased processing times.

Real-Time Data Access Considerations

• The limitations of AI models like Claude are highlighted, particularly regarding internet access and real-time data retrieval.

• Many users seek solutions that incorporate real-time data feeds, but it's essential to evaluate whether this feature aligns with actual use cases.

Multi-tool Strategies in AI Development

• As AI portfolios expand, understanding when to utilize different tools becomes crucial for optimizing performance and results.

• While APIs may offer flexibility, they often come with trade-offs such as higher latency compared to direct interactions with custom GPT interfaces.

Evaluating API Performance

• The complexity API has notable speed issues and does not consistently deliver high-quality search results compared to its web counterpart.

• Users must weigh the benefits of detailed control over outputs against the potential loss of automation when using complex APIs.

User Preferences and Solution Design

• End-user preferences significantly influence tool selection; some users prefer immediate results while others value involvement in the process.

• Understanding user sacrifices is vital for determining the most suitable solution—whether it be a simple custom GPT or a more sophisticated API approach.

Conclusion on Use Cases for Custom Solutions

Exploring Custom GPTs and Tools for AI Solutions

Importance of MVP Testing with Custom GPTs

• The speaker emphasizes the effectiveness of using custom GPTs for rapid testing of ideas, suggesting it as a starting point for developing solutions.

• They highlight that custom GPTs can be very quick to implement, especially if search functionality is not required, making them a versatile tool depending on user needs.

Challenges in Using Automation Tools

• The discussion points out that while tools like Make and Zapier are accessible, they require a certain level of expertise to navigate effectively.

• The speaker warns that users may face difficulties when trying to modify prompts or troubleshoot issues without adequate knowledge of these platforms.

Personal Note on Hurricane Milton

• A personal note is shared regarding Hurricane Milton, expressing hope for the safety of those affected in Florida and reflecting on the speaker's own experience during the storm.

Upcoming Discussions on Notebook LM

• The speaker mentions an upcoming session focused on Google's Notebook LM, indicating its relevance in recent AI discussions and hinting at its various use cases.