Transitioning an AI demo to production is a daunting task, requiring months of effort and expertise, particularly in enterprise contexts. While creating a demo AI might seem straightforward, the journey to production is quite complex. Most off-the-shelf solutions don’t meet specific enterprise needs, necessitating extensive customization and refinement.
Enterprise challenges with AI adoption
Enterprise adoption of AI presents unique challenges, especially in sectors requiring accuracy and factuality beyond marketing and content generation. Compliance requirements in industries like healthcare and finance demand precise handling of data, making it difficult to rely solely on generic AI solutions. Additionally, some tools may not align with immediate enterprise needs due to privacy concerns and data sensitivity.
Generative vs Extractive approaches
Generative AI approaches, while effective in many scenarios, face limitations in enterprise use cases. Issues like rephrasing and hallucination hinder their applicability, particularly in delivering factual FAQ-like answers. In such cases, an extractive QA approach or traditional information retrieval solution may be more suitable.
Implementing Guardrails in Production
Implementing guardrails in production is essential to ensure accuracy and compliance. Enterprises require a layer for fact-checking, data verification, and sensitive information handling. Guardrails you build must include mechanisms for fact-checking, data verification, and data visibility segmentation to meet enterprise standards.
Layered Data Ingestion Approach
Handling diverse data formats requires a layered approach to data ingestion. Combining new tools like Azure Form Recognizer with traditional methods like web crawlers enables comprehensive data handling and processing.
Optimizing Production Processes
Optimizing production requires advanced techniques such as meta-data enrichment and auto-eval for accuracy monitoring. These techniques ensure the reliability and performance of AI systems in real-world scenarios.
Load Balancing for LLM APIs
Load balancing for LLM APIs is crucial due to provider rate limits and potential network outages. An LLM API load balancer monitors health and reroutes requests to ensure seamless operation.
Auto-Evaluation Frameworks
Auto-eval frameworks are essential for detecting improvements or failures in LLMs due to model updates. These frameworks enable continuous monitoring and adaptation to ensure AI systems remain effective and up-to-date.
Product Readiness Considerations
Product readiness considerations extend beyond prompt engineering to encompass input protection, dynamic prompt programming, and monitoring of LLM query latency. Various tools and models, including Microsoft Presidio for data protection and Azure OpenAI for embedding and summarization, are utilized to ensure robust and reliable AI applications.
Importance of Software Engineering
Building production-ready AI apps requires significant software engineering expertise, with AI being just a small part of the process. DevOps and Platform Engineering are essential for ensuring scalability, reliability, and performance in real-world deployments.
We hope this helps you in your own AI journey. All the best from Team Atomicwork.
By Kiran Darisi, CTO, Atomicwork
Meet the Atomicwork team at stand 595 at SITS, The Service Desk & IT Support Show, on 17-18th April 2024, ExCeL London. You can now register for your free ticket here.