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Why Custom Models Are Key For Successful Enterprise Generative AI Adoption

Generative AI applications such as ChatGPT and Midjourney have gained widespread popularity due to their extensive knowledge bases. However, as businesses consider adopting generative AI, they must address the limitations of large language models (LLMs) and customise them to suit enterprise IT architectures and use cases. Untrite, an AI software company specialising in decision intelligence platforms, understands the challenges enterprises face and aims to provide custom models and taxonomy training using company internal data. This article explores the hurdles organisations encounter when implementing generative AI and highlights the importance of overcoming these limitations through tailored models and infrastructure considerations.

Infrastructure Challenges and Compute Resources

As the demand for generative AI soars, organisations face the daunting task of providing the necessary infrastructure for model training and operations. Building optimised infrastructure for massive models like GPT-4, with billions of parameters, can be both costly and technically challenging. Balancing security and compliance requirements with the computational resources needed for scalable generative AI remains a key concern for enterprises. Private cloud deployments offer an alternative for organisations with strict compliance regulations, allowing them to keep data within their own networks. Additionally, certain use cases, such as low-latency requirements or handling large volumes of real-time data, may necessitate on-premises deployments to ensure optimal performance.

Open Source vs. Proprietary Models

To address infrastructure challenges, the open-source community has emerged as a strong proponent of open AI models. Open sourcing AI models enables collaboration and knowledge sharing, benefiting developers and organisations with limited resources. With baseline models accessible through API endpoints, users can fine-tune and customise models on their own data. Open-source models also hold the potential to reduce bias in AI systems through diverse input and democratise access to generative AI capabilities. However, enterprises may require proprietary models to experiment and adopt generative AI with minimal risk. Proprietary models offer a controlled environment for exploring use cases without compromising security or privacy. Customisation and sandbox testing within an enterprise setting ensure better alignment with specific needs and mitigate potential risks.

Fine-tuned Models for Enterprise Use Cases

Instead of relying on large, generalised models like GPT-4, organisations are increasingly considering smaller, specialised models fine-tuned for specific enterprise use cases. These narrower models offer advantages such as higher accuracy and easier evaluation, catering to businesses’ stringent requirements. Moreover, smaller models are more agile, easier to integrate into existing infrastructure, and can be tailored to organisations’ data, enhancing their suitability for enterprise adoption. Customising baseline models empowers enterprises to have greater control, extensibility, and flexibility in their generative AI systems. By building specialised models that align with specific workflows and needs, enterprises can mitigate risks such as hallucinations, topic drift, and susceptibility to prompt injection often associated with LLMs.

As the demand for generative AI continues to grow, enterprises face the task of overcoming the limitations of LLMs while ensuring secure and customised implementations. Untrite recognises the need for tailored generative AI solutions and provides decision intelligence platforms that leverage custom models and taxonomy training on company internal data. By addressing infrastructure challenges, considering the pros and cons of open source vs. proprietary models, and fine-tuning models for enterprise use cases, organisations can safely and effectively adopt generative AI. Evaluating individual use cases and understanding associated risks is paramount for enterprises to harness the potential of generative AI while navigating the complexities of this rapidly evolving field.

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