Common Challenges in LLMOps Adoption

AI in real-world applications is often just a small black box; The infrastructure surrounding the AI black box is vast and complex. As a product builder, you will spend disproportionate amount of time dealing with architecture and engineering challenges. There is very little actual AI work in large scale AI applications. Leading a team of outstanding engineers who are building an LLM product used by multiple enterprise customers, here are some lessons learned: Architecture: Optimizing a complex architecture consisting of dozens of services where components are entangled, and boundaries are blurred is hard. Hire outstanding software engineers with solid CS fundamentals and train them on generative AI. The other way round has rarely works. UX Design: Even a perfect AI agent can look less than perfect due to a poorly designed UX. Not all use cases are created equal. Understand what the user journey will look like and what are the users trying to achieve. All applications do not need to look like ChatGPT. Cost Management: With a few cents per 1000 tokens, LLMs may seem deceptively cheap. A single user query may involve dozens of inference calls resulting in big cloud bills. Developing a solid understanding of LLM pricing and capabilities appropriate for your use case and the overall application architecture can help keep costs lower. Performance: Users are going to be impatient when using your LLM application. Choosing the right number and size of chunks, fine-tuned app architecture, combined with the appropriate model can help reduce inference latency. Semantic caching of responses and streaming endpoints can help create a 'perception' of low latency. Data Governance: Data is still the king. All the data problems from classic ML systems still hold. Not keeping the data secure and high quality can cause all sorts of problems. Ensure proper access and quality controls. Scrub PII well, and educate yourself on all applicable regulations. AI Governance: LLMs can hallucinate and prompts can be hijacked. This can be major challenge for an enterprise, especially in a regulated industry. Use guardrails are critical for any customer-facing applications. Prompt Engineering: Very frequently, you will find your LLMs providing answers that are incomplete, incorrect or downright offensive. Spend a lot of time on prompt engineering. Review prompts very often. This is one of the biggest ROI areas. User Feedback and Analytics: Users can tell you how they feel about the product through implicit (heatmaps and engagement) and explicit (upvotes, comments) feedback. Setup monitoring, logging, tracing and analytics right from the beginning. Building enterprise AI products is more product engineering and problem solving than it is AI. Hire for engineering and problem solving skills. This paper is a must-read for all AI/ML engineers building applications at scale. #technicaldebt #ai #ml

According to IBM's latest report, the number one challenge for GenAI adoption in 2025 is... data quality concerns (45%). This shouldn't surprise anyone in data teams who've been standing like Jon Snow against the cavalry charge of top-down "AI initiatives" without proper data foundations. The narrative progression is telling: 2023: "Let's jump on GenAI immediately!" 2024: "Why aren't our AI projects delivering value?" 2025: "Oh... it's the data quality." These aren't technical challenges—they're foundational ones. The fundamental equation hasn't changed: Poor data in = poor AI out. What's interesting is that the other top adoption challenges all trace back to data fundamentals: • 42% cite insufficient proprietary data for customizing models • 42% lack adequate GenAI expertise • 40% have concerns about data privacy and confidentiality While everyone's excited about the possibilities of GenAI (as they should be), skipping these steps is like building a skyscraper on a foundation of sand. The good news? Companies that invest in data quality now will have a significant competitive advantage when deploying AI solutions that actually work. #dataengineering #dataquality #genai

Researchers at the University of Victoria identified 7 obstacles to adopting GenAI tools in engineering organizations: 1. Fear of decreased skills. Developers worry about over-reliance on AI tools and the loss of learning opportunities. 2. Limited AI capabilities. AI tools often lack awareness of the operational environment and codebase, which limits their effectiveness. 3. Lack of prompting skill. Developers need to experiment with AI tools to get desired results, leading to potential frustration and decreased usage. 4. Potential judgment from others. Some fear being judged by peers for using AI tools, and this can hinder their adoption. 5. Not having a culture of sharing. Lacking a supportive culture for sharing AI tool practices can slow adoption.  6. Cost of tools. High costs and limited access to AI tools can be a barrier. 7. Lack of guidelines. Without clear guidelines and training, developers may struggle with how to use AI tools effectively. Addressing these challenges can improve the adoption and effective use of GenAI tools in engineering organizations. Read more findings from this study in today’s newsletter: