The release of ChatGPT ignited a tidal wave of innovation and buzz around Artificial Intelligence, particularly Large Language Models (LLMs). In under a year, AI has moved from research labs and niche use cases into the core of startup and enterprise strategies. While trends ebb and flow—like ChatGPT’s dip in users mid-2023—the momentum behind LLM-powered product development shows no signs of slowing down.
From content generation to coaching tools, startups are tapping into LLMs to create real value. At Roaring Infotech, we’ve partnered with several forward-thinking teams building products in this space. Whether you're a founder, product manager, or curious engineer, here are seven practical lessons we've learned while building with LLMs.
If you're building an MVP, don’t reinvent the wheel. Start by using API access to established models like OpenAI’s GPT-4. This lets you validate your product concept quickly and cheaply, using only prompt engineering to explore feasibility.
Training your own model or fine-tuning an open-source version can be powerful later—but in the early stages, it's expensive, time-consuming, and likely to become outdated fast. Focus first on discovering if you’re solving a real, monetizable problem.
While OpenAI is the go-to for many, alternatives like Anthropic’s Claude are worth testing too. Different models may outperform others depending on your specific task. Tools like Vercel’s AI playground make it easy to compare model performance side-by-side.
In fact, one product doesn’t need to rely on just one model:
The key takeaway? Build your architecture to be flexible so you can plug in new models as they emerge.
A common misconception: “We need machine learning experts to build AI products.” That’s no longer true. Today, a skilled full-stack developer can build powerful LLM-based applications using frameworks like LangChain.
LangChain connects databases, APIs, and prompts into chains that power chatbots, smart assistants, and more—without deep ML expertise. It lowers the barrier to entry so your team can focus on user experience and functionality.
In LLM products, prompts = UX. Much of your product’s effectiveness depends on how well prompts are structured in natural language.
Engineers may build the backend logic, but prompts should be written and tested by skilled communicators—product managers, marketers, copywriters. Use few-shot prompting (with good and bad examples) to guide the model's output, and encourage non-technical team members to get involved in iteration.
LLMs can be unpredictable. Even a small change in a prompt can lead to dramatically different output—so iteration is essential.
Traditional dev cycles release every few weeks. With LLM-based products, you can ship updates every 1–2 days. Use OpenAI’s playground to prototype quickly. Treat your LLM like a living system that improves with continuous testing and tuning.
You can’t improve what you don’t measure. For every prompt, feature, or flow, define what success looks like.
Build lightweight feedback systems—star ratings, thumbs up/down, or short text inputs—to collect user insights. At Roaring Infotech, we often create internal dashboards to evaluate and refine model output using real feedback. This not only enhances the current experience but also informs future ML training.
LLM APIs bill by tokens, and costs can add up fast. GPT-4 may deliver better results, but it’s also more expensive. When building your product, always consider:
Start modeling your usage early to avoid surprise bills down the road.
LLM product development is still in its early days—which is exactly what makes it so exciting. From MVP to scale, success comes from speed and creativity in iteration.
Whether you're building a smart chatbot, personalized assistant, or data-driven tool, LLMs have lowered the barrier to entry and raised the ceiling of what's possible.
Got a product idea or looking for guidance on implementing LLMs? Let’s talk.