Tight budgets? Rigid code beats flexible code for AI projects
When building AI agents or LLM-powered tools, hardcoding specific logic for today's needs can be cheaper and more reliable than designing for flexibility. Abstraction layers add complexity and token costs that can outweigh their benefits for solo builders.
Conventional software wisdom says flexible, reusable code is always better. But when you're building pipelines where an LLM reads, generates, or modifies code, that flexibility comes with a real cost: more code means more tokens, and more tokens means more money per run.
Rigid code — written to do exactly one thing, with values hardcoded rather than abstracted — is shorter, easier for an LLM to parse, and leaves less room for misinterpretation. For a solo developer or small team on a tight budget, over-engineering for future use cases that may never arrive can drain resources fast. The post argues that the classic YAGNI principle ('You Aren't Gonna Need It') becomes even more important when an LLM is part of your execution loop.
Key points
- Hardcoded, specific code uses fewer tokens when passed to an LLM, directly lowering API costs
- More abstraction layers make it harder for an LLM to understand and correctly modify code
- The YAGNI principle — only build what you need now — is especially valuable in AI-driven pipelines
- Flexible architecture makes sense for large teams or long-lived projects, but is often overkill at the solo stage
- Keeping code simple and explicit reduces LLM errors and speeds up iteration
Quick term guide
- AI agents
- AI agents are AI tools that can carry out steps toward a goal, not just answer once.
- hardcoding
- Writing a specific value or behavior directly into the code, making it simple and clear but harder to change later.
- abstraction layer
- A piece of code that sits between two systems and lets them work together without either one needing to know the other's details.
- traction
- Proof that real people or companies are using or paying for a product.
- token costs
- Token costs are the fees paid for the text an AI model reads and writes.
- token cost
- The money or usage spent when sending text to an AI model and getting text back.
- pipeline
- An automated sequence of steps that processes or moves data without manual intervention.
- API costs
- Fees paid when software calls an online service programmatically.