How to Avoid a GenAI Proof of Concept Graveyard

Generative AI (GenAI) is the hottest topic in technology today, with companies across industries scrambling to build their own GenAI proof of concepts (POCs). Organizations are trying out use cases, with larger enterprises running multiple POCs. This widespread experimentation indicates a universal eagerness to harness GenAI’s potential for the enterprise. 

Building a GenAI POC is not difficult. It’s a simple three-step process: choose a cloud provider (e.g. Azure, AWS, Google), pick a popular large language model (LLM) from the cloud provider, and develop a POC using the popular retrieval-augmented generation (RAG) pattern to optimize the output.  

However, many POCs end up gathering dust in the "POC graveyard," failing to progress to production.  

In our experience building numerous GenAI proof of concepts, we have identified the most common pitfalls that lead to POC failure and how to overcome them to ensure GenAI experiments thrive. 

Beyond the Hype: Picking the Right Use Case 

One key reason POCs falter is that they are based on a poorly chosen use case. Don't get caught up in the hype by building a POC for the sake of creating one. Select a business-led use case with a clear return on investment (ROI) and align it to your core business performance metrics. Focus on operational efficiency, cost savings, new revenue streams, or fostering innovation. Use a strategic, long-term lens rather than a short-term sizzle. Focus on “clear win” scenarios that offer high value and are achievable within your resource constraints (budget, data, skills, time).  

Related:Addressing Trending Questions About Generative AI

Tech Stack: Focus on Integration, Not Obsession 

While the entire GenAI tech stack might seem complex, don’t get stuck over-analyzing the selection of every technology component. Most cloud providers offer similar LLM options, and the technologies are constantly evolving. Pick the most reasonable, popular LLM without too much emphasis on scientific evaluation. Bigger and better models are being released every week. Ensure the architecture is flexible enough for leveraging APIs and swapping out key components with new ones. 

Focus on the bigger challenge of bringing everything together: integrating data, prompt engineering, scalability, observability, and other security and governance tools for an effective solution.  

Data is King: Quality Over Quantity 

Insufficient or poor-quality data can be a major roadblock. Develop a data strategy that prioritizes data quality and governance. Data readiness should target high-value data over aiming for comprehensiveness. Ensure that you have the legal and intellectual property rights to use the data powering your AI application. 

Related:9 Future of Work Concepts That Need More Attention

Curb the Hallucinations With Accuracy and Control 

Senior management has concerns about AI limitations like hallucinations and biases, which can lead to false or skewed outputs. While no perfect solution exists, combining a few techniques can mitigate these risks.  

Most GenAI applications built on the popular RAG pattern provide enterprise context to the generic LLMs. Building RAG is easy, but building quality RAG is hard. Basic RAG applications can return incorrect, irrelevant information. They can also suffer from poor performance and inefficient use of expensive resources, or they cannot scale to large volumes of enterprise data. You can optimize RAG through the development lifecycle using smart chunking, embedding model choice, vector database choice, semantic cache, etc. 

Prompt engineering can significantly improve accuracy. Involve domain experts in this process to ensure the LLM understands your enterprise context adequately. Emphasize high-quality training data, limit response lengths to control outputs, and seek citations to improve accuracy. Build guardrails to route complex questions to human experts and maintain human oversight through model monitoring and user feedback. 

Related:Squeezing the Maximum Value Out of Generative AI

Data Privacy: Addressing Security Concerns 

Data security remains a significant challenge with GenAI. Properly securing your data in the cloud with appropriate access controls is critical. You should also filter out private information in the data or from the LLM output. For next-level security, consider self-hosted LLMs. This involves downloading an open-source LLM like Llama 3 and running it on your private infrastructure. This approach provides you with better control over data privacy and security but burdens you with the responsibility of managing the infrastructure.  

AI Ethics: Building Trust and Transparency 

Management needs to be confident that their AI initiatives are ethical and responsible.  

To scale GenAI in the enterprise, you must establish an LLMOps platform with the right Responsible AI practices embedded throughout the development process. This will ensure that ethical and other considerations, such as explainability, are built in. Forming a GenAI center of excellence (COE) can drive this governance across the organization. 

Beyond the POC: Realistic Cost Estimation 

Unexpected costs can derail projects. The LLM price is just 10% to 15% of the overall cost. Get a thorough cost estimation of the build and ongoing maintenance costs. Build costs include data preparation and storage, embedding model and vectorization, vector store, security and compliance, cloud hosting, development, testing, and prompt engineering. Maintenance costs are higher than the build cost and should include application sustenance, data refresh, upgrading or swapping to new LLMs, monitoring, observability, user support, and training. 

Avoiding a POC graveyard requires strategic planning, careful selection of impactful use cases, a strong data strategy, and addressing concerns around accuracy, privacy and ethics. By avoiding common pitfalls, organizations can turn promising POCs into successful, production-ready GenAI solutions that drive significant business value.