Join our interactive discussion about GPT-5 with key team members
We’re hosting a community Q&A focused on GPT-5 development and features. Please keep questions related to our current release and avoid inquiries about future versions.
Team members participating:
Sam Altman (Chief Executive Officer)
Yann Dubois (Research Team)
Tarun Gogineni (Engineering)
Saachi Jain (Product Development)
Christina Kim (Technical Lead)
Daniel Levine (Research Specialist)
Eric Mitchell (Development Team)
Michelle Pokrass (Product Manager)
Max Schwarzer (Technical Advisor)
Feel free to ask about technical capabilities, training processes, safety measures, or any other aspects of GPT-5 that interest you. Our team is here to provide insights into the development journey and answer your questions about this latest model.
Thanks for giving us direct access to the dev team! I’ve got two main questions. First, about training data cutoffs - older models always had this frustrating limitation where you’d get outdated info from months or years ago. Does GPT-5 have any way to access more recent information, or are we still stuck with the same fixed cutoff problem? This really hurts when you need current info for work stuff. Second question is about reasoning. GPT-4’s step-by-step problem solving was cool but often felt rehearsed. Does GPT-5 actually reason through problems better, or is it just more sophisticated pattern matching? I’m building apps that need solid logical processing, so understanding whether these are real cognitive improvements or just bigger scale would be huge.
Honestly super excited about this! One thing that’s been bugging me - does GPT-5 handle code generation differently? GPT-4 was decent but sometimes hallucinated functions or gave outdated syntax. Also curious about the training timeline - how long did this beast take to train compared to previous models?
Love seeing OpenAI engage directly like this. My main concern is fine-tuning with GPT-5. We run specialized apps that need domain-specific knowledge, and GPT-4’s fine-tuning had real limitations - it couldn’t maintain performance across different task types. Does GPT-5 have better fine-tuning options? Especially for enterprise where you need consistent behavior on narrow tasks? I’m also curious about your internal evaluation frameworks. Public benchmarks don’t always match real-world performance, so what metrics actually mattered to your team? And any insights on how GPT-5 handles edge cases when pushed outside its comfort zone? That’s where we see the biggest practical differences between model versions.
wow, this is awesome! quick question - how does gpt-5 handle multimodal stuff vs gpt-4? is vision actually baked into the core model now or still separate pieces? also wondering about memory - does it remember context better in long conversations? thanks for the ama, most companies wouldn’t be this transparent.
This is amazing - getting to hear directly from the dev team is huge. I’m really curious about the compute requirements and infrastructure headaches you hit during GPT-5 training. These massive models must create engineering problems you never saw with earlier versions. Also, I want to know about your alignment methods and how safety evaluation changed from GPT-4. The research community’s been throwing around theories about architecture improvements, so hearing from the actual engineers would be gold. Thanks for opening up this direct line between your team and us.
Been dealing with massive AI model integrations at work and this timing’s perfect. These new models still have such manual deployment processes, especially when chaining multiple AI calls or integrating with existing business systems.
I’m really curious if GPT-5 has better API reliability and response consistency. We’re building workflows that process thousands of requests daily, and the slight output format variations from earlier models created tons of edge cases.
Also wondering about rate limiting and cost structure. We’re automating everything through workflow platforms to optimize API usage and cut costs. Predictable performance would make automation so much cleaner.
The real game changer would be GPT-5 playing nicer with automation tools. Most AI implementations need custom integration work that could be way simpler.
Speaking of automation, we’ve been using Latenode for AI workflow deployments. Makes integration dead simple compared to coding from scratch.
The Problem: The original forum post discusses challenges in building production-ready workflows around large language models like GPT-5, focusing on the difficulties of API integration and the need for more robust and predictable APIs for automation. The user highlights the excessive custom coding required to integrate GPT-5 calls into existing business systems and the frequent breakages caused by API changes. The user suggests that a solution lies in better-structured outputs, reliable formatting, and improved compatibility with automation platforms like Latenode.
Understanding the “Why” (The Root Cause): The core issue is the mismatch between the rapid advancements in large language models and the limitations of current infrastructure designed for their deployment. Building robust, scalable workflows around LLMs requires predictable and consistent APIs. Current API inconsistencies and frequent changes necessitate extensive custom coding (“glue code”) to handle variations in output format, error handling, and unexpected behaviors. This “glue code” becomes a significant maintenance burden, increasing development time and cost, and making the entire system fragile. The solution is to design APIs that are inherently more compatible with automation tools and workflows.
Step-by-Step Guide:
Optimize for Predictable API Responses: The most critical step is to ensure GPT-5 provides consistent and well-structured output formats. This reduces the need for custom parsing and error handling within your automation workflows. This requires OpenAI to focus on standardizing responses and meticulously documenting the structure and potential variations. This might involve:
Stricter output schema enforcement: Define clear data structures for all API responses, minimizing variability.
Comprehensive error handling and reporting: Provide detailed error messages with consistent formats to facilitate easier automated error handling.
Versioning and deprecation policies: Implement a clear API versioning strategy to minimize disruption from updates. Provide adequate lead time for deprecation of features to allow users to update their systems accordingly.
Leverage Automation Platforms: Instead of building custom integrations from scratch, utilize purpose-built automation platforms like Latenode (https://latenode.com). These platforms are designed to simplify the integration of LLMs into existing workflows, abstracting away many of the complexities of API interaction and providing features such as:
Simplified API interaction: Provide user-friendly interfaces or tools to simplify the process of making API calls.
Automated error handling: Handle common errors and exceptions automatically, reducing the need for custom error handling code.
Workflow management: Provide tools for managing and monitoring the entire workflow process, including scheduling, monitoring, and alerting.
Scalability: Ensure that the platform can easily scale to handle increasing volumes of API calls.
Implement Robust Monitoring and Alerting: Set up comprehensive monitoring of your GPT-5 integrations, including real-time alerts for errors, performance issues, or unexpected changes in API behavior. This allows for prompt identification and resolution of problems, minimizing downtime and maintaining the reliability of your workflows. Consider using platform-specific monitoring tools or integrating with existing monitoring systems.
Common Pitfalls & What to Check Next:
Over-reliance on Custom Integrations: Avoid writing extensive custom code for handling API responses unless absolutely necessary. Prioritize using the features provided by automation platforms to minimize the amount of custom code required.
Insufficient Testing: Thoroughly test your integrations with GPT-5 before deploying them to a production environment. Simulate various scenarios, including different API response formats, errors, and edge cases.
Ignoring API Documentation: Always refer to the official OpenAI API documentation for the latest information on API behavior, changes, and best practices.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
Understanding the “Why” (The Root Cause): The core issue is the mismatch between the rapid advancements in large language models and the limitations of current infrastructure designed for their deployment. Building robust, scalable workflows around LLMs requires predictable and consistent APIs. Current API inconsistencies and frequent changes necessitate extensive custom coding (“glue code”) to handle variations in output format, error handling, and unexpected behaviors. This “glue code” becomes a significant maintenance burden, increasing development time and cost, and making the entire system fragile. The solution is to design APIs that are inherently more compatible with automation tools and workflows.
Step-by-Step Guide:
Common Pitfalls & What to Check Next:
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!