The field of large language models (LLMs) has advanced rapidly over the past few years. However, there’s a world of difference between a cool demo and a reliable, scalable LLM product. In this article, I’ll blend lessons from the practices with my personal favorite tools and frameworks, from vector databases and agents to API endpoints and deployment strategies.
Foundations of Building Reliable LLM Products
1.1 Prompt Engineering: The Key to Unlocking LLMs
When I started experimenting with LLMs, I quickly realized that effective prompting is one of the most critical—and often underestimated—components of working with these models. In essence, the prompt is how you communicate with the AI, and the better you structure it, the better results you’ll get.
One method I’ve found particularly useful is in-context learning, where you provide examples within the prompt to teach the model what kind of output you expect. This has been a game-changer in projects like recommendation engines, where giving a few examples of personalized suggestions made the system much more effective. However, it took some experimentation to get this right—finding the perfect balance between too few and too many examples. In my experience, five examples is a great starting point for most cases, but this number may vary depending on the task complexity.
Another crucial technique is Chain-of-Thought (CoT) prompting. It’s especially useful when dealing with complex tasks that require more than just a simple response. For instance, when building a customer service assistant, CoT helped the AI walk through its reasoning before delivering the final answer. This drastically improved accuracy, especially when summarizing customer interactions. For anyone new to LLMs, starting with simple, well-structured prompts and breaking tasks into smaller steps is crucial.
Takeaway: Think of prompts as instructions for a task. Refine them like you would a conversation with a new team member—clear, concise, and broken down into manageable steps.
1.2 Leveraging RAG to Address LLM Limitations
One challenge many teams face when working with LLMs is ensuring that the model outputs accurate, up-to-date, and domain-specific information. This is where Retrieval-Augmented Generation (RAG) comes in handy. Instead of expecting the model to “know” everything (which can lead to hallucinations or outdated responses), RAG lets the model pull in relevant, real-time information during the generation process.
I often use RAG to complement my LLMs, especially in knowledge-heavy applications where accuracy is paramount. ChromaDB is my go-to tool for rapid prototyping because it’s built on SQLite, making it lightweight and easy to set up. However, when scaling up for serious projects, Supabase with PGVector is the ideal solution. It offers all the features I need—role management, backups, APIs—while also allowing for self-hosting if necessary.
RAG not only improves the quality of outputs but also addresses the common problem of hallucinations (where the model makes up information). In my experience, integrating a solid RAG pipeline into LLM applications, particularly for domain-specific tasks, can make all the difference in delivering reliable, grounded answers.
Takeaway: Use RAG to feed your LLMs real-time, relevant data. Tools like ChromaDB for prototyping and Supabase for larger-scale projects offer the flexibility and reliability you need.
1.3 Managing Complexity: Breaking Down Tasks for Better Results
When building AI systems, one of the lessons I’ve learned is that complexity is often the enemy of reliability. Early on, I made the mistake of asking LLMs to handle too much in a single go—such as extracting information, checking its accuracy, and then generating a response—all in one prompt. The results were inconsistent.
Now, I advocate for a modular approach: breaking down tasks into smaller, manageable steps. For example, when working on a conversational AI system, we split the process into three stages: first, extract key details from the conversation, then verify their accuracy, and finally synthesize them into a coherent response. This approach allows for more control over each step and makes it easier to tweak the system for improvements.
Takeaway: Don’t overwhelm your AI with too much information at once. Break down complex tasks into smaller, focused prompts, and handle each step separately.
Turning LLM Experiments into Scalable Systems
2.1 Data Quality: The Core of Successful AI
While LLMs are powerful, they’re only as good as the data they’re fed. In the early days of my career, I focused more on refining models than on data quality. However, I quickly learned that even the best models fail when trained or used with poor data. This is especially true for LLMs, where subtle differences in input format, consistency, or domain knowledge can lead to wildly different outputs.
For example, in one of my projects involving customer support ticket analysis, we found that while the model worked perfectly on clean, well-organized data, it struggled when faced with the messiness of real-world tickets, filled with typos and incomplete information. Regularly reviewing and cleaning the input data became critical. I’ve also found “vibe checks”—informal reviews of input-output samples—extremely helpful for catching issues early.
Takeaway: Never underestimate the importance of data quality. Regularly review, clean, and ensure your data reflects the real-world conditions your model will encounter.
2.2 Tools for Building and Deploying LLM-Based Systems
In my journey, I’ve tested various tools to build and deploy LLM-powered systems. Here’s a quick rundown of what I currently rely on:
- FastAPI and Pydantic for backend development. FastAPI is fast, easy to use, and comes with features that simplify LLM application development, including database ORM, CORS support, and multi-threading. Pydantic ensures strict typing and data validation, which makes the application more resilient.
- Docker for deployment. Containerizing LLM applications makes scaling and deployment easier. Docker images ensure repeatability, meaning once your app is containerized, you can run it on any system. Tools like Coolify simplify the deployment process further by providing SSL management, automated CI/CD pipelines, and backup options—all in a user-friendly interface.
- LangChain and LlamaIndex for agent-based workflows. I’ve found LangChain particularly useful when building complex workflows where the LLM needs to reason through multiple steps or use external tools. For more data-heavy tasks like RAG pipelines, LlamaIndex offers faster and more efficient indexing.
Takeaway: Choose your stack based on usability and scalability. FastAPI, Docker, and LangChain are just a few of the tools that can streamline LLM development and deployment, making the process more efficient.
2.3 Observability: Tracking and Evaluating LLMs in Production
Once your LLM-based application is live, observability becomes essential. You need to track, evaluate, and monitor your model’s performance—whether it’s measuring the accuracy of responses, tracking user interactions, or identifying bottlenecks in response times.
I use LangSmith for tracking and evaluating LLM outputs, particularly during the prototyping phase. Its intuitive system for tracking inputs and outputs, along with fast evaluation cycles, makes it ideal for smaller-scale projects. However, for larger, more data-intensive applications, Arize Phoenix offers more robust tracking options, especially if you need to monitor performance over time.
Takeaway: Make sure your LLM applications are observable. Use tools like LangSmith for tracking during development and Arize Phoenix for production-scale monitoring.
Long-Term Considerations for Building AI Products
3.1 Self-Hosted vs. API-Based LLMs: The Right Choice for Your Needs
A question I often get from clients is whether they should go the self-hosted route for LLMs or rely on API-based solutions. My answer depends on the specific needs of the project.
If privacy is a major concern, self-hosting with a platform like Ollama is the best choice. Self-hosting ensures complete control over the data and avoids sending sensitive information to third-party providers. On the flip side, for projects that require fast deployment without stringent privacy needs, OpenRouter is my preferred platform. It aggregates multiple models into one easy-to-use interface, offering flexibility and avoiding vendor lock-in.
Takeaway: Evaluate your privacy and performance needs before deciding between self-hosted or API-based LLMs. Ollama offers excellent privacy, while OpenRouter provides flexibility and ease of use.
3.2 Prioritize Usability and Avoid Vendor Lock-In
As someone who has spent years building products, I’ve become increasingly aware of the risks of vendor lock-in. While using a major cloud provider like Azure or AWS can offer a lot of out-of-the-box services, it also ties you into their ecosystem. This is why I often recommend starting with VPS-hosted solutions or open-source tools like Coolify to avoid dependency on any single provider.
By using open-source tools, you gain more control over your environment, reduce long-term costs, and retain flexibility to pivot when needed. Coolify, for instance, allows you to manage databases, front-end, and back-end deployments, and set up automated CI/CD pipelines without being tied to a large provider.
Takeaway: Prioritize flexibility and usability. Avoid vendor lock-in by choosing open-source or VPS-based solutions when starting out.
Conclusion: Combining Practical Tools with Strategic Thinking for LLM Success
Building LLM-powered products is a journey—one that requires not just technical expertise but also practical, long-term thinking. Over the years, I’ve learned that while LLMs offer tremendous potential, they come with their own set of challenges, from managing data quality and designing prompts to deploying scalable systems and ensuring observability.
By using the right tools—like FastAPI, LangChain, Docker, and Coolify—and keeping long-term strategic considerations in mind, you can create powerful, user-friendly AI products that scale with ease and flexibility. Whether you’re self-hosting LLMs for privacy reasons or leveraging API-based solutions for speed, the key is to stay adaptable, keep experimenting, and always put usability at the center of your decisions.
The lessons shared here, combined with my favorite tools, should give you a strong foundation for building smarter, more reliable LLM applications. Happy building!
