Key challenges in building gen AI apps
Even with a highly capable LLM at its core, a production-grade generative AI app often encounters challenges in three main areas:
- quality
- control
- cost
In practice, teams must tackle all three challenges simultaneously to run gen AI apps in production.
Building production-level quality
- Unpredictable performance: LLMs can produce inconsistent or unexpected results. A prompt that works one day might fail the next if the model or context changes.
- Response accuracy and safety: Developers must ensure responses are both correct and safe. Incorrect outputs (hallucinations) or harmful and offensive content can damage user trust, brand reputation, or even violate regulations.
- Defining “high quality”: Domain experts often need to contribute their specialized knowledge to evaluate outputs and refine prompt logic. This collaboration requires tooling that nontechnical stakeholders can use.
Control of data and models
- Data leakage: Sensitive customer or enterprise data can inadvertently leak through model outputs if proper guardrails and sanitization steps are not enforced.
- Governance and ownership: Many organizations already have data governance protocols or compliance requirements, for example SOC2 or HIPAA. Integrating LLMs into these frameworks can be complex, especially if the model is externally hosted.
- Observability: Teams need to track every request, response, and intermediate action in the application to audit model decisions or troubleshoot errors. Without robust logging and tracing, it’s hard to maintain compliance or root-cause issues.
Cost at scale
- Cost vs. quality: LLM-based solutions can become expensive at scale - especially when using more advanced or reasoning models. Teams must weigh the higher cost against the performance gains, often employing caching or specialized model routing to stay within budget without sacrificing quality.
- Developer time and complexity: Beyond model inference costs, building robust gen AI apps can be time-consuming, especially when incorporating multiple components like retrievers, structured databases, and third-party APIs. Minimizing developer effort requires streamlined workflows and automated testing.