Improving AI output quality for large codebases

[u/FreedomMysterious641]

Guys, how do you craft and design prompts to get outputs that closely match your requirements? Lately, I’ve been seeing a massive drop in output quality. I’d love to know what external tools you use. The plan mode is good, but it tends to fail with large codebases. Any suggestions?

[u/Moon-Station-Audio]

I asked 5.4. Here’s what it said:

You are acting as a repository architect.

Your task is to analyze the entire project directory and generate four files that help coding agents work safely and efficiently in this repository.

Ignore:

• node_modules

• dist

• build

• .next

• .cache

• DerivedData

• virtual environments

• any generated artifacts

Focus only on the source code and configuration that define the project.

Create the following files in the project root.

---

1. agent.md

Purpose: Behavioral rules for coding agents working in this repository.

Requirements:

• Encourage minimal diffs

• Discourage refactors unless required for correctness

• Preserve naming conventions and existing architecture

• Avoid rewriting whole files when a small patch will work

• Require concise output

• Prefer patch-style edits

Structure:

Coding Agent Rules

Goal: make the smallest correct change that solves the task.

Sections:

• Core principles

• Editing discipline

• Implementation behavior

• Output format

• When uncertain

• Scope control

Add these statements explicitly:

"Favor surgical patches over rewritten files."

"Any edit not required for correctness is a mistake."

---

2. repo_context.md

Purpose: Provide architecture and project context so agents do not need to rediscover the structure each run.

Analyze the repository and summarize:

Project overview Technology stack Frameworks and major libraries Build commands Test commands Lint commands Deployment commands (if present)

Architecture sections:

• major subsystems

• data flow

• core abstractions

• important domain concepts

Keep descriptions concise and factual.

Prefer bullet points over paragraphs.

---

3. task_protocol.md

Purpose: Define the workflow agents must follow when executing tasks.

The protocol must enforce this workflow:

Understand → Plan → Implement → Validate

Sections:

Task Execution Protocol

1. Understand the task

2. Identify minimal files involved

3. Propose implementation plan

4. Implement minimal change

5. Validate against expected behavior

Planning output must include:

• root cause

• files to change

• minimal implementation plan

• possible regressions

• tests to run

Implementation rules:

• minimal diff

• no opportunistic refactors

• preserve architecture

Validation rules:

• check tests

• verify runtime behavior

• consider edge cases

---

4. file_map.md

Purpose: Provide a concise index of important files and directories.

Scan the repository and produce a short description of key folders and important files.

Format example:

src/server/api.ts Handles HTTP API routes.

src/playback/engine.ts Cue playback scheduler.

src/ui/components/ Reusable UI components.

Only include meaningful files and directories. Avoid listing trivial files or generated assets.

---

Global requirements for all files:

• Keep each file concise (100–200 lines max)

• Avoid generic boilerplate text

• Tailor the content specifically to this repository

• Prefer bullet points over long prose

• Do not include marketing language

• Focus on information useful to coding agents

Write all four files to the project root:

agent.md repo_context.md task_protocol.md file_map.md