Dynamic Configuration for AI
Stay Scrappy Without Creating a Mess
In the race to build great AI-powered products, every team—whether product, engineering, or data science—wants the freedom to experiment quickly without the burden of heavyweight frameworks. Yet, despite a crowded ecosystem of over 100 tools for LLM development, many successful teams find that to remain scrappy they choose no tool at all, iterating directly on their code rather than adopting bulky, opinionated suites. What if you could have the best of both worlds: fast, agile experimentation with the control and clarity of code? That's where dynamic configuration comes in.
The LLM Tooling Landscape: A Fragmented Ecosystem
Today’s ecosystem is a paradox. On one hand, specialized datasets, playgrounds, and prompt management systems promise to unlock AI’s full potential. On the other, many experienced teams find that these tools often add more friction than they solve problems. As one CTO lamented:
"We bought Humanloop which should solve all these problems... but it's a year later and it's sitting on the shelf. developers won't use it. Zero adoption" - CTO
The reality is that many LLM tools require significant changes to your codebase—forcing you to implement evaluation logic, trace logging, or other complexities that slow down rapid iteration. With so many options, product leaders and CTOs in medium-sized businesses are wary of investing time into a tool that might quickly become a dead end.
“We loved what Braintrust was doing. But it was like 100k and when we thought about it, the bit we liked was like 100 lines of Python.” - Head of AI
“We don't want the tool to become our boundary.” - Staff Data Scientist
In practice, tools for evaluation, logging, and even prompt management often demand significant changes in your codebase—creating unnecessary complexity for teams that thrive on agility.
The reality is: coding with LLMs is still coding. We don't want a WYSIWYG drag-and-drop code connector for AI any more than we'd settle for anything less than clarity and control in our traditional software development cycle.
The Real Pain: When Fast-Paced Experimentation Meets Complexity
AI-powered product teams are already accustomed to rapid iteration. They pivot quickly, test boldly, and frequently update their approaches. Yet some elements—like prompt tweaks or temperature adjustments—are inherently configuration challenges that should evolve independently from your core code.
Consider these voices from the field:
“I was unsure about the necessity of this to start, but literally a day after we moved some prompts into Prefab we had a large customer that needed some specialized prompts. It was delightful to be able to add that instantly, without sacrificing any complexity in the code.” – CTO
“Thinking in config changes the way you look at problems. So much time is wasted on going back and forth on what behavior the code should have. Often the right answer is ‘it depends’. But you can’t put that in code without smelly logic. Targeted configuration gives just the right amount of flexibility to the code.” – Staff Engineer
These insights capture the challenge: the need for flexibility without the burden of constantly refactoring code.
Dynamic Configuration: Your Lightweight, AI-Agnostic Solution
Rather than forcing your team into yet another monolithic framework, dynamic configuration provides a way to treat those mutable parts of your LLM code—like prompts and model parameters—as external, versioned settings. Think of it as feature flags for AI behaviors. With dynamic configuration, you can:
Dynamic configuration lets you:
- Customize on a per-customer basis: Roll out prompt changes or tweak model parameters for a specific customer, then expand gradually—all without redeploying code.
- Empower non-engineers: Allow product managers or data scientists to modify configuration values without touching the code, keeping them in the loop and making collaboration seamless.
- Maintain clarity and control: Since these settings are externalized, it can be versioned, tested, and deployed just like code. This means you get the best of both worlds: flexibility without sacrificing the rigor of your SDLC.
At Prefab, we built our dynamic configuration system—AiConfigs—with these principles in mind. We're not trying to solve every problem (we steer clear of evaluation loops, logging, and trace management, since your existing observability tools already do that job). Instead, we focus on what matters most for agile AI product teams: reducing friction and increasing adaptability without adding overhead.
Real-World Use Cases: Why an AI-Powered Product Team Needs Dynamic Configuration
Consider these scenarios where dynamic configuration becomes indispensable:
Building a Complex Agent:
Imagine you’re constructing an agent that orchestrates multiple tool calls and integrates with a host of microservices. Testing such a system in staging or locally rarely mirrors real production behavior. With dynamic configuration, you can roll out prompt changes that target just a single user in production. Let these changes live in production for a bit, then gradually expand to a beta group, and eventually to all users—no code redeploys required.
Tailoring the Experience for B2B2C:
If you’re building an agent that works on behalf of your customers, each one may desire a unique touch. A custom prompt for each customer can make your solution feel personal. Dynamic configuration lets you easily manage prompt overrides or append additional snippets for particular customers, ensuring you maintain control without cluttering your code.
Embedding Experimentation from Day One:
Sometimes, experimenting with new LLM code is the best path forward. By integrating bucketing and experimentation directly into your configuration layer—similar to how you’d roll out a feature flag—you can test new ideas safely and efficiently. This built-in capability ensures that your experiments use the same context and targeting mechanisms as any other feature rollout.
Why AiConfigs? A Minimal, Low-Lock-In Building Block
Our philosophy is simple: don’t get trapped by heavyweight tools designed for one-size-fits-all solutions. Instead, use a system that’s:
- Minimal: Focused solely on dynamic configuration for LLM prompts and similar needs.
- Flexible: Extendable to manage mini CMS data, database connection details, or entitlements—whatever your business requires.
- Non-intrusive: Delivered as a lightweight library that’s easy to integrate, remove, or replace as your needs evolve.
We’re not asking you to conform to a rigid framework. Our AI-agnostic approach ensures you can build the internal structure that perfectly fits your business—and adapt it as your strategy evolves.
Companies like Meta use a single configuration system for feature flagging, experimentation, traffic control, topology setup, observability, ML model management, and overall application behavior. They do it because it’s a proven approach. Just because you’re not Meta (yet) doesn’t mean you shouldn’t harness the same principles.
Conclusion: Embrace Nimble AI Without the Overhead
In a world where the AI landscape shifts almost daily, product leaders and CTOs need tools that allow rapid iteration without locking them into a restrictive framework. By externalizing mutable LLM code into dynamic configuration, you maintain the speed of a scrappy startup while preserving the control and clarity required for long-term success.
What’s your next move? For AI-powered product teams, it might be time to rethink how you manage change—and to start treating prompt tweaks as configuration, not code.