Building efficient Gen AI programs requires more than just deploying a model — it demands a structured approach that balances performance, cost, and reliability. Understanding how to optimize workflows will make the difference between a well-functioning AI solution and one that drains resources. In this article, we’ll explore the key principles and best practices to help you maximize the efficiency of your Gen AI programs.
How to build efficient Gen AI programs?
1) Break the problem into smaller sub-tasks. 🏗️
First, not all tasks require the most powerful LLM. Break the problem into smaller sub-tasks, use traditional programming techniques for data-heavy processes and user interface rendering, leverage small models for simpler AI tasks like sentiment analysis and spelling correction, and reserve the heavy lifting for larger models only when necessary.
2) Implement LLM Orchestration. ⚙️
Orchestration becomes crucial because LLMs are slow. Streaming, parallelism, and speculative execution can help speed up the process. For instance, while Midjourney shows a half-finished image as it generates, you still need to wait for the full result to evaluate it. The same goes for using LLMs to determine function parameters—you can’t execute the function until all parameters are ready.
3) Establish robust error handling. 🚨
Error handling is another essential component. You need mechanisms to detect when an LLM has produced a wrong or harmful output and decide whether to retry, adjust, or abandon that task. Many commercial LLMs have built-in safety layers that cut off offensive content during streaming (a.k.a. safeguarding).
4) Evaluate your LLM setup regularly. 🕵🏻
Evaluating the effectiveness of an LLM setup is like running continuous integration tests. You need robust evaluation to ensure reliability, given the numerous ways to configure and prompt an LLM. The eval systems may even involve other LLMs, which require their own tuning. Just like with automated tests, you need to rerun the evaluation benchmarks after every configuration change to catch issues before they reach customers.
5) Monitor costs closely. 💸
Monitoring costs is crucial too—FinOps must be part of Gen AI from day one. Whether using a hosted LLM or running one on your own GPUs, managing expenses is a significant challenge, especially as you scale.
Conclusion
The good news? You don't need to be an AI researcher to use LLMs. Off-the-shelf models, standardized APIs, and abundant tutorials have made it possible for traditional developers to build Gen AI applications.
Incorporating Gen AI into applications opens up vast possibilities, but it requires oversight to ensure efficiency. As AI technology continues to evolve, mastering these fundamental practices will empower developers and organizations to build robust, scalable, and sustainable Gen AI solutions.
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