You’re claiming not to use ChatGPT or any similar generative AI model? Fascinating. Even global tech giants like Apple, with their immense resources, are integrating ChatGPT and similar models into their ecosystems. Yet, you’re suggesting that you’ve bypassed such industry-standard tools to develop a custom NLP system capable of advanced emotional intelligence, multimodal communication, and real-time interactions?
If that’s the case, let’s see the proof. What proprietary framework are you using? How was it trained? What dataset powered this innovation? If it’s as groundbreaking as you claim, why hasn’t the AI research community or major players in the field taken notice? Transparency is key, and vague mentions of Azure AI Foundry won’t cut it.
Show us the benchmarks, the model architecture, and the inference pipeline. Are you really running a state-of-the-art NLP model independently, or is this just a façade for a ChatGPT API wrapper dressed up with marketing buzzwords? If even Apple is leveraging such tools, it’s hard to believe you’re somehow ahead of them without a massive infrastructure and decades of research backing your claims.
- Can you explain the architecture of the AI model you’re developing?
- Are you using a transformer-based architecture or something custom? What optimizations have you implemented for scalability?
- What is the size and diversity of your training dataset? How are you ensuring that the data isn’t biased or unbalanced?
- What techniques are you using for fine-tuning the model? Are you employing LoRA or PEFT?
- What specific loss function are you using to train your model, and why is it suitable for your application?
- Are you incorporating Reinforcement RLHF like OpenAI? If so, how are you gathering and validating the feedback?
- What cloud service or on-premises infrastructure are you using? Can you detail the server specs, including GPUs and networking configurations?
- How are you handling horizontal and vertical scaling for inference under high demand?
- What techniques are you using to minimize latency for real-time interactions?
- How are you ensuring fault tolerance and high availability in your infrastructure?
- Are you using REST or GraphQL for your APIs? How are you securing your endpoints against misuse or DDOS attacks?
- Which tokenization scheme does your model use? Are you employing BPE or SentencePiece?
- What rate-limiting strategies are you implementing to prevent abuse while maintaining fair access?
- How are you making your model explainable and interpretable to users?
- What steps have you taken to audit and mitigate biases in your model?
- Are you keeping audit logs for model interactions? How do you ensure these logs are secure?
- Given the high cost of training and maintaining models, can you break down how funds are allocated to training, infrastructure, and development?
- Are you leveraging any open-source pre-trained models or building everything from scratch?
- Do you have a GitHub, GitLab, or Bitbucket repository where we can see your development progress or contributions?
- Do you maintain technical documentation in your repository for the backend, APIs, and system architecture?
- Are you sharing any details about your AI model, such as architecture, training methodology, or datasets? Are pre-trained models or checkpoints available for community use?
- Are any components of your system open source? If so, could you provide links to the repositories?
- Can we see active commits, issue tracking, and collaboration on your GitHub? Are you inviting contributions from the community?