Practical Prompt Engineering Lessons from Using Claude Code

What Worked: Shipping Production Code
The user, a project manager without a development background, successfully built and shipped production code using Claude Code. The resulting application runs in a browser and includes over 1,200 tests.
The Core Challenge: Prompt Quality
The primary difficulty identified was that Claude Code will produce poor or incorrect results if prompts are not precise. The user states it "will absolutely let you walk into a wall at full speed if your prompt isn't airtight."
Three Techniques That Improved Results
- Two-Phase Prompts: Instead of writing prompts in one pass, the user adopted a two-phase approach. Phase 1 involves writing the what in your own domain language, including rules and desired outcomes. Phase 2 involves rewriting the prompt from the perspective of a reliability engineer, adding verification gates, single objectives, explicit session boundaries, and anti-shortcut rules. The user found that one phase yields decent results, but two phases yield production-grade results.
- One Prompt = One Objective: Bundling multiple goals into a single prompt consistently led to poor outcomes. Claude Code would prioritize one goal, merge them sloppily, or provide incomplete solutions for both. The user emphasizes "ruthless scope discipline" with one objective per prompt, calling this the biggest "quality multiplier" discovered.
- Specific Role Definitions: Generic role instructions like "Act as a senior developer" were found to be "nearly useless." Effective roles must name the exact combination of expertise required for the task. The user provides an example: "Conservatory-trained music theorist who has built commercial composition engines" produces fundamentally different and better results than a vague instruction like "music expert." Specificity changes the model's underlying thinking process, not just the tone of the output.
📖 Read the full source: r/ClaudeAI
👀 See Also

Debugging OpenClaw + Ollama Local Model Timeouts: Five Fixes for Silent Failures
A developer identified five root causes for OpenClaw agents silently timing out with local Ollama models like Gemma 4 26B, including a blocking slug generator, a 38K character system prompt, and hidden timeouts. The fixes involve disabling hooks, modifying configs, and adjusting Ollama settings.

Cost-Effective OpenClaw Multi-Agent Setup Using Subscription Models
A Reddit user describes routing all OpenClaw multi-agent operations through existing $200 Anthropic Pro Max and $200 ChatGPT OpenAI Codex subscriptions instead of raw API calls, using cheaper Anthropic models for simple agents and more complex models for others.

Building a Fully Local Multi-Agent Assistant with OpenClaw and Ollama
A developer shares their stack for a fully local personal AI assistant using OpenClaw and Ollama, including models qwen3.5:35b-a3b, gemma3:4b, mistral:7b, MCP servers for Home Assistant and Gmail, and a Telegram Bot interface.

Scaling Agentic Coding to 150+ PRs/Week: Lessons from $85K in Tokens at Lovable
Alexander Lebedev shares how he scaled from 20–30 PRs/week with one human to 150+ PRs/week with a swarm of AI agents, spending $85K in tokens since January. Key learnings: risk classification, AI review replacing human code review, and the challenge of preserving knowledge diffusion.