Finding the Balance - Deterministic Logic and LLM Flexibility in Huckleberry
In developing Huckleberry, we've committed to walking a careful line between deterministic code and the adaptable, agentic power of Large Language Models (LLMs). It's not about choosing one over the other. It's about staying steady, step by step, in a system where consistency and creativity both matter.
The Role of Deterministic Logic
Deterministic code provides consistency and predictability. For parts of the system where the outcome must always be reliable, such as parsing structured data or enforcing workflow integrity, hard-coded logic is the right choice. It behaves the same way every time, no matter who's asking or when. You could say it keeps us from straying when things get unpredictable.
Our task manager core relies on deterministic processes, creating unique task IDs, tracking completion status, maintaining data integrity in JSON files. These aren't places where we want creative interpretation.
Harnessing LLM Flexibility
LLMs excel at working with unstructured data, understanding nuance, and interpreting intent. They are especially useful when the problem space is fuzzy, such as summarising long documents, suggesting next steps, or understanding a vague user prompt. But this flexibility comes with trade-offs. They do not always stay true. Ask the same question twice and you might get a different answer.
This is where Huckleberry's integration with VS Code's AI assistants shines. When an LLM interprets what the user wants, converts natural language into structured tasks, or suggests which task to tackle next, we're leveraging the right tool for the job.
Striking the Right Balance
The real design challenge is not whether to use LLMs or not. It is deciding where to trust them, and where to set the rules ourselves. We do not assume we will get it right the first time. We build, test, and adjust. When something wobbles, we ask: should this have been a fixed rule, or should it have been flexible? That judgement is where the real work lives.
For example, managing task state in Huckleberry is deterministic. We want to know exactly when something was created, completed, or changed. But interpreting what to do next is a job for the agent. That is where flexibility helps, and where it is fine if every answer is not identical, as long as it moves the work forward.
A Practical Example
Consider how Huckleberry handles task creation:
- LLM Flexibility: When a user types "I need to implement authentication for my API", the LLM interprets this intent and decides a task should be created
- Deterministic Logic: Once the decision is made, our code handles the creation process, generating an ID, timestamping, formatting the JSON, updating indexes
- LLM Flexibility: When suggesting related tasks or next steps, the LLM can consider the broader context of the project
- Deterministic Logic: The actual state changes and storage of these relationships follow strict rules
This dance between flexibility and determinism gives users both consistency where it matters and helpfulness where creativity is beneficial.
Holding the Line
We are not chasing complexity for its own sake. We are building a system that can be trusted. One that is predictable when it needs to be, and responsive when that is what matters. Sometimes that means holding firm. Sometimes it means letting the system interpret the moment.
And sometimes, it just means walking the line.
As we continue developing Huckleberry, this balance remains core to our philosophy. Task management needs reliability, but it also benefits tremendously from the intuitive understanding that modern AI brings to the table.
How are you balancing deterministic logic and AI flexibility in your projects? We'd love to hear from you.