[FEATURE]: Optimize prompts using principle constraints and DSL compression

[d0lwl0b]

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Describe the enhancement you want to request

Problem

OpenCode's prompts are verbose and sometimes contradictory, causing:

• Unnecessary token consumption
• Unpredictable LLM outputs
• Difficulty maintaining consistency

Core Solution: Two Complementary Approaches

1. Principle-Based Constraints

Instead of describing behaviors explicitly, reference established design principles from LLM training data:

• UNIX Philosophy (for modular, single-purpose components)
• KISS Principle (for simplicity)
• YAGNI (to avoid over-engineering)
• SOLID (for object-oriented design)

Example:
Instead of: "Make it simple, don't add unnecessary features, focus on one thing..."
Use: "Apply KISS and YAGNI principles."

Benefits:

• Reduces token count by 60-80%
• Eliminates instruction conflicts via internally consistent frameworks
• Leverages LLM's existing knowledge

2. DSL Context Compression

Create structured templates to filter noise in extended conversations:

[CONTEXT_SUMMARY]
CORE_ISSUE:: <main problem>
KEY_POINTS:: <bullet points>
ACTION_ITEMS:: <next steps>
[END_SUMMARY]

Information Theory Rationale:

• Acts as entropy-reducing encoder
• Implements lossy compression preserving semantic essence
• Filters low-information noise

Why This Works

Both methods address root causes:

• Principles compress complex concepts into single references
• DSL templates enforce structure, eliminating ambiguity
• Together they create concise, predictable prompts

Suggested First Steps

1. Audit current prompts for most redundant sections
2. Replace verbose descriptions with principle references
3. Design 2-3 DSL templates for common workflows
4. Test with critical paths

This approach transforms prompt engineering from art to science—reducing costs while improving output quality.

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Additional Context

The specific principles, paradigms, or conventions mentioned are not exhaustive or exclusive—they are examples of a broader pattern. You can search for and adopt any well-established, widely recognized design principles, methodologies, or standards relevant to your domain (e.g., "separation of concerns," "immutable architecture," "12-factor app"). The key is leveraging consensus-based constraints that exist within the LLM's training corpus. This approach reduces rule conflicts inherent in human language and minimizes token waste caused by over-explanation.

Additionally, custom DSLs can be co-designed with the LLM itself. After several rounds of discussion, you can instruct the LLM to summarize the conversation using a mutually agreed-upon DSL format. This practice effectively filters noise and compresses context, saving tokens for subsequent interactions.