Q&A: Prompt Engineering & System Prompts — Tuning CactusRalph-Coder's Agent Behavior

[AGI-Corporation]

Prompt Engineering & System Prompts Deep-Dive Q&A

This thread is for deep technical questions about how to craft, tune, and customize the prompts that drive CactusRalph-Coder's agent behavior — from system instructions to few-shot examples to chain-of-thought techniques.

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Common Questions

System prompt structure

• Where is the system prompt defined, and how do I modify it?
• What is the default system prompt for the Planner, Coder, and Reviewer agents?
• How do I inject project-specific context into the system prompt at runtime?
• Are system prompts versioned or tracked in the codebase?

Tuning agent behavior

• How do I make the agent more conservative (fewer hallucinations, safer code)?
• How do I make the agent more creative or exploratory for greenfield projects?
• Can I configure the agent to always follow a specific coding style or linting standard?
• How do I instruct the agent to prefer certain libraries or avoid deprecated ones?

Few-shot & in-context examples

• How do I add few-shot examples to guide code generation?
• Where do I define example input/output pairs for the coder agent?
• How many examples can I include before hitting context limits?
• Can examples be loaded dynamically from a vector store based on the current task?

Chain-of-thought & reasoning

• Does the agent use chain-of-thought prompting by default?
• How do I enable or disable step-by-step reasoning traces?
• Can I see the agent's internal reasoning before it produces code?
• How does scratchpad reasoning affect token usage and latency?

Multi-turn conversation prompts

• How does the agent maintain prompt continuity across a multi-turn coding session?
• How do I reset the conversation context without losing the project state?
• Can I inject a correction mid-session (e.g., "that approach won't work because...")
• How are user feedback turns structured in the prompt history?

Model-specific prompt tuning

• Do prompt templates need to be different for GPT-4, Claude, and Gemini backends?
• How do I handle instruction-following differences between open-source and closed models?
• Is there a prompt that works consistently across all supported LLMs?

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Share your prompt engineering discoveries, system prompt snippets, and tuning tips below.