8 Best Practices to Understand and Build Generative AI Applications Effectively

Generative artificial intelligence (GenAI) is making waves across various industries, promising to revolutionize how we handle data and automate complex tasks. But amidst all the excitement, there's also a fair amount of hype, ethical concerns, and technical hurdles to navigate.

Big tech companies and startups alike are pouring resources into AI research and development, hoping to harness its potential for boosting efficiency, improving decision-making, and unlocking new revenue streams. But the hype often overtakes reality, leading to unrealistic expectations—and some misconceptions—about what AI can actually do.

This situation is a bit like the early days of crypto: both AI and crypto started as disruptive technologies—but unlike cryptocurrency, which faced fundamental economic concerns and a lack of real-world use cases people could connect with, AI has seen broader adoption and investment because of its real-world applications and success stories, especially in natural language processing (like us with Query Assistant).

These successful implementations have shown AI’s practical benefits and potential for innovation. Companies that have successfully integrated GenAI into their operations are reaping the rewards in terms of competitive advantage and operational efficiency, but those who have done so often limited their scope and focused on specific and narrow problems to solve.

In this guide, we’ll help you get there by sharing eight best practices to help your organization understand and build GenAI applications more effectively.

1. Set realistic expectations on augmentation vs. automation

One key thing to remember in AI development is knowing the difference between augmentation and automation. AI is fantastic at augmenting human capabilities, like interpreting natural language, recognizing patterns in huge amounts of data, and making predictions that fall under some confidence interval. But fully automating every task is still out of reach. So, when you're working on AI projects, it's better to think about how AI can improve what you already do rather than trying to replace human-centered processes altogether.

2. Leverage traditional software engineering practices

Building reliable AI applications is a lot like traditional software engineering. Key practices like testing, monitoring, and debugging are essential to make sure AI models work as they should. Machine learning engineers and software developers should work together to tackle issues with data quality, model accuracy, and system scalability.

3. Invest in understanding data for your teams

The single biggest difference in building products that leverage AI is that teams need to spend a lot of time understanding and improving their own data. The quality of your AI products ultimately hinge on the quality of your data, whether you’re practicing basic context-gather in prompting workflows, have a RAG pipeline, or are even considering fine-tuning a foundation model to your use case. Understanding your data is also not just a matter of poring through big CSVs at regular intervals: it requires you to invest in good instrumentation that captures real-world inputs and outputs from your users, then analyzes patterns in that data, feeding it into your development cycles.

4. Spend your calories on the right focus areas

One of AI’s biggest strengths is its ability to turn natural language with contextual data into meaningful structured outputs. This “text to JSON” use case is based on the Retrieval Augmented Generation (RAG) design pattern, which is the backbone of most AI products today. However, information retrieval is a deep problem space, where different products require different solutions to search and rank results before being passed along to a generative model to produce a result. Developing an understanding of different retrieval approaches and carefully developing evaluation systems to score their quality over time will pay dividends in the future.

5. Invest in evaluations + understanding your users’ behaviors

Creating effective AI applications isn't just about calling a generative model and developing creative prompts; it also involves disciplined development practices and a deep understanding of what users need. After developing a proof of concept, invest heavily in a robust evaluation system based on data, and evolve this evaluation system over time based on real-world inputs and outputs from your users.

Use evaluations to drive what makes a good response for a user based on what they’re trying to accomplish. Other common mistakes include not experimenting with different prompting or retrieval approaches and overlooking the importance of data you collect in production compared to an offline experimentation environment. Tackling these issues early on is crucial for delivering reliable, user-friendly AI solutions.

6. Use observability to understand production behavior

Improving the quality of AI applications doesn’t just involve evaluations. There is always a big difference between how your systems behave in a lab environment compared to the real world, and you need good observability to understand how. Comprehensive logging and tracing to capture inputs, outputs, and errors are a good place to start.

You can extend this by tracing every step of a retrieval pipeline that happens upstream of a generative model call, allowing you to correlate when a good or bad retrieval result leads to a particular language model output.

7. Deploy early and often

In the words of German field marshal Helmuth von Moltke (1800–1891), “No plan of operations extends with certainty beyond the first encounter with the enemy’s main strength.” Whatever expectations you develop based on what you observed in a development environment are unlikely to remain once your AI product has first contact with your users. There’s no hope in predicting what they might do or how your product will react. Instead, you need to release as you can—with evaluations in hand—and continually use production data to build out your evaluations, influence problems you solve, and redeploy to observe new behavior. Establishing a data flywheel from production to development back to production is key to long-term success. Eventually, with enough iteration, your product will do its job effectively for most users, at which point you can more passively monitor it.

8. Bring ML engineers into product development teams

We said it earlier, but creating successful GenAI applications relies on teamwork between machine learning engineers and software developers. Although there is a natural breakdown of responsibilities—ML engineers are often most concerned with evaluations, fine-tuning jobs (if fine-tuning), and retrieval experiments—there must not be a silo between ML engineers, software engineers, and product managers. This is similar to the popular “triad” model of modern product development, where a product manager, designer, and lead engineer collaborate closely on end-to-end concerns. For AI applications, ML engineers must also be heavily involved in the product development lifecycle, and other disciplines should work together on evaluations, data quality, and experiments with different retrieval systems or ML models. In turn, ML engineers should concern themselves not just with specifics about ML models and data quality, but understanding user behavior, and getting a handle on overall system health and product behavior in production.

Building the future of AI

It's clear that diving into GenAI is an exciting yet challenging journey. The potential for AI to revolutionize various industries is enormous, but it’s important to keep things grounded. Think of AI as a tool to enhance what humans do best, rather than replacing them entirely. Stick to tried-and-true software engineering practices and make sure your team truly understands and improves the data you're working with.

Focusing on the right areas, like using evaluations to evolve retrieval and prompting in the RAG design pattern, can really pay off. It's also crucial to build systems that evolve based on real-world feedback. Don't just rely on lab results; keep an eye on how your AI behaves in the wild and adjust accordingly.

Bringing together ML engineers, software developers, and product managers can create a well-rounded approach to building effective AI applications. Keep deploying, learning from real user interactions, and iterating on your product. This way, you’ll continually improve and eventually create something that meets user needs effectively.

As you embark on your GenAI adventure, keep these best practices in mind.