GenAI Engineering Horror Stories (And How to Avoid Them)

A predictive machine learning app is already facing engineering challenges with generative AI use cases adding new hurdles such as hallucinations and complex manual workflows. To avoid these issues, retrieval-augmented generation (RAG) can be used to improve an LLM's responses by referencing a knowledge base provided by the business domain, but implementing RAG can take a huge amount of complex manual work as scaling up. Another challenge is that traditional RAG may not provide enough personalized and up-to-date responses for production use cases, requiring additional context like structured data sources. Furthermore, providing too much information to the system prompt can introduce irrelevant context and slow down the system. To overcome these challenges, tools like Tecton can be used to automate RAG components, manage prompts as code, and provide an agentic workflow that enables the LLM agent to choose relevant data on demand, allowing for smarter GenAI app deployment.

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