Reasoning Anchoring: Making Models Think Before They Speak

The Problem with Generic CoT

Chain of Thought (CoT) is great, but generic models use generic logic. When asking about a specific platform, generic logic often leads to wrong conclusions.

Reasoning Anchoring

We force the model to "anchor" its reasoning in the specific context of our platform before it attempts to answer.

The Pattern

We structure our training data like this:

User: [Question] Assistant:

Identify Intent: User wants to [Intent]

Check Platform Capabilities: Does FineTune Lab support [Intent]? - Yes/No check based on knowledge base.

Formulate Plan: If yes, steps are A, B, C. If no, alternative is D. [Answer]

Why It Works

By forcing this specific structure, the model learns to "lookup" its internal knowledge about the platform before generating the final response. It reduces the chance of it guessing based on general training data.

Results

- Consistency: Responses follow a predictable structure.

Accuracy: The "Check Platform Capabilities" step acts as a self-verification mechanism.