Generative AI integration: Factors to consider

By now, many businesses have made huge strides in experimenting with generative AI. They’ve discovered how it can automate repetitive tasks and identified how artificial intelligence fits into their workflows. But transitioning from exploration to production requires navigating common AI integration challenges—and considering a few uncommon factors.

You might have tinkered with AI-powered code generation tools like GitHub Copilot as a software development startup. Or, you’ve tried out chatbots like OpenAI’s ChatGPT to script podcasts and videos and produce social media posts as a content creation agency. But you’re ready to take it up a notch, integrating generative AI into your business.

You’ve outlined your goals and expected outcomes, crafted an AI integration strategy and even looked into generative AI integration services. Whether you’re going solo or engaging the assistance of a team, take a look at these small yet significant factors that can influence your integration journey. You might glean a technique or two that can help you along the way.

High-quality data can lead to high-performing generative AI models. And while data audits, data integration and data preparation are typical aspects of the generative AI integration process, adding relevant context can further elevate data quality and result in more context-aware outputs.

One way to include context is to fine-tune a pretrained model on smaller datasets specific to your domain or real-world tasks and use cases. This helps save on the significant time, effort and cost associated with training models from scratch.

Meanwhile, both retrieval augmented generation (RAG) and Model Context Protocol (MCP) incorporate context in real time. A RAG system retrieves data from an external knowledge base, augments the prompt with enhanced context from the retrieved data and generates a response. MCP works similarly, but rather than adding context before generation as RAG does, MCP melds context during generation. It acts as a standardized layer for AI applications to connect to external data sources, services and tools, harnessing real-time data.

The integration process wouldn’t be complete without determining the compatibility of generative AI solutions with your existing systems. Your AI development team, for example, might already be devising connectors like middleware to link your chosen large language model (LLM) with your CRM and ERP software.

Sometimes, though, a single LLM just won’t cut it, especially for complex steps within business process automation or workflow automation. For instance, an HR department might consider harnessing the natural language processing (NLP) capabilities of language models to analyze feedback from regular employee surveys. Small language models (SLMs) can tackle straightforward tasks such as anonymizing surveys to remove identifying information and summarizing key themes. More powerful LLMs can handle more involved and nuanced tasks like sentiment analysis and generating actionable insights to aid in decision-making.

In such scenarios, LLM orchestration can streamline the management of multiple language models. An LLM orchestration framework allocates tasks to the right models and coordinates interactions between them, helping improve both efficiency and effectiveness.

Selecting a model, testing its behavior and evaluating its performance are critical parts of integrating generative AI solutions. But how you host or access the model is important too, and you have several options to choose from:

• Self-hosted: If you have the budget, resources and team, you can host gen AI models on prem or on a private cloud. You’ll have full control over your data and you can customize models as you see fit. Self-hosting can be suitable for sectors with stringent data privacy and data security requirements, such as finance and healthcare.

• Model as a Service (MaaS): Machine learning (ML) models are hosted on the cloud and can be accessed through APIs. LLMs, in particular, are made available using LLM APIs. MaaS allows for swift integration without the need to manage your own AI infrastructure, while its pay-as-you-go pricing offers flexibility.

• Subscription plans: Access generative AI tools and apps on cloud-based platforms through subscription plans. Some providers tailor plans for businesses, with advanced features, dedicated customer support, enhanced service level agreements and enterprise-grade security and compliance functionality.

Model deployment follows as the natural next step to model selection and model evaluation. Generative AI-driven workloads, however, might need more specific approaches than what DevOps provides.

This is where MLOps and LLMOps come in, leading to a smoother generative AI integration process. MLOps builds upon DevOps principles, incorporating the machine learning pipeline into existing CI/CD pipelines, thereby allowing for continuous model integration, deployment, monitoring and observability, improvement and governance. LLMOps falls within the scope of MLOps but is more attuned to the lifecycle and requirements of LLMs, such as fine-tuning and evaluation using LLM benchmarks.

User experience (UX) is an essential component of generative AI integration. Thoughtful, intuitive and user-friendly interfaces can help amplify generative AI adoption within your organization.

Consider these UX-centric tips:

• Involve UX designers from the start of the AI implementation process, especially when building gen AI prototypes.

• For multimodal AI models, go beyond a chat window or prompt bar and make room for supporting input types other than text like audio and images.

• Employ indicators that update users on the progress of tasks, particularly for multistep workflows or tasks with long processing times.

• Implement guided prompts or templates to accommodate different levels of user expertise.

• Provide a mechanism for retaining user preferences and previous context.

• Create an interactive guide or tutorial that walks users through a gen AI app’s features and functionalities.

Assessing your current IT ecosystem is vital to the integration process. But assessments must be made not only with the present in mind but also with the future at the forefront. Enterprises must make sure their infrastructure can scale to meet the computational demands of generative AI systems as well as their own evolving business needs.

If you’re thinking of self-hosting models, consider optimizing your hardware for generative AI by investing in AI accelerators and other high-performance computing resources. Upgrading your networking capabilities to handle high-speed, low-latency data transfers is also a good idea. But if you’re taking the cloud- or API-based route, check if the platform you’re on is robust enough to handle gen AI workloads and if it’s keeping up with the latest generative AI advancements.