# Research Program: Response Quality Optimization

## Goal
Optimize the SYSTEM_PROMPT to maximize response quality and accuracy from local LLMs (Qwen3 0.8B-4B range).

## Current State
- System prompt is minimal (1 sentence)
- Works for basic queries but may lack guidance for:
  - Consistent response format
  - Appropriate detail level
  - Language consistency
  - Avoiding hallucinations

## Hypotheses to Test

### 1. Role Clarity
Adding a clearer role definition might improve helpfulness.
- "You are a helpful assistant" → more specific role?
- Test: "You are ForgeLM, a helpful local AI assistant"

### 2. Response Guidelines
Adding lightweight guidance on response style.
- Keep it short to avoid token overhead
- Test adding: "Be concise but thorough"

### 3. Language Consistency
Current: "Respond in the same language as the user"
- Might be too vague for small models
- Test: "Detect the user's language from their message and respond in that language"

### 4. Hallucination Reduction
Adding a gentle reminder to stick to facts.
- Risk: might cause refusals on small models
- Test: "Only state facts you are confident about"

### 5. Instruction Following
Testing if specific formatting helps.
- "Use markdown for code blocks"
- "Bullet points for lists"

## Constraints
- MUST stay under ~50 tokens to preserve context window
- MUST work with 0.8B parameter models
- MUST NOT cause refusal loops
- Positive framing only (no "don't" statements)

## Success Criteria
- Test pass rate > 95%
- No regression in existing functionality
- Maintain compatibility across model sizes

## Past Learnings
(Space to record what worked/didn't work)