Projects for Coders

AI Projects for Coders

Connect code, docs, screenshots and tickets so AI understands the repo before it answers.

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The simple idea

Do not ask AI to guess your work. Give it the project.

Developers already know the problem: AI can write a decent isolated function, then fail when the answer depends on project architecture, conventions, existing files, package choices or old decisions.

A MultipleChat Project lets you import repo files from GitHub, upload documentation, add screenshots or error logs, and create project instructions that explain the stack. The AI can then retrieve relevant chunks before answering instead of guessing from a vague prompt.

What to upload

Start with the files the answer depends on.

People often fail with AI Projects because they upload too little. If a human expert would need the source material, the project needs it too.

GitHub repository files, important modules and config files

README files, architecture notes and API documentation

Error logs, stack traces and failing test output

Screenshots of UI bugs, product requirements or Figma notes

Database schemas, SQL files, JSON examples and integration docs

Project instructions

Tell the project how your profession thinks.

The same files can produce very different answers depending on instructions. Set expectations once so every model knows how to handle sources, uncertainty and format.

Respect the existing architecture and naming conventions.

Do not invent files or APIs. Ask when repository context is missing.

Prefer small patches and explain tradeoffs before large refactors.

When uncertain, identify which source file or config needs inspection.

Separate quick fix, robust fix and long-term refactor options.

Playbooks

Five workflows to run first.

These are not theoretical feature descriptions. These are the first practical workflows a developers should try after creating a project.

Workflow 1

Create a codebase project

Start with the README, core modules, package files, config and architecture docs. If you connect GitHub, keep imported repo files scoped to the project.

Prompt to try

Read the project files and summarize the architecture. Identify the framework, main data flow, important directories, external services, and likely risk areas.

Workflow 2

Debug with logs and source files

Upload the error log plus the relevant files. The project can search the code and logs together, which is much stronger than pasting only the stack trace.

Prompt to try

Use the project files and this stack trace to find the likely root cause. Give me the minimal patch, the files affected, and one test I should run after changing it.

Workflow 3

Explain unfamiliar code

When joining a repo or revisiting old work, ask for a map of the codebase, then drill into specific modules.

Prompt to try

Explain how authentication works in this project. Cite the relevant files, describe the request lifecycle, and list any security risks or unclear assumptions.

Workflow 4

Compare implementation options

Use AI Comparison or AI Collaboration inside the project to get different implementation strategies grounded in the same codebase.

Prompt to try

We need to add role-based permissions. Compare three implementation approaches for this codebase: minimal change, clean architecture, and migration-friendly. Include risks and affected files.

Workflow 5

Turn requirements into a development plan

Upload product notes, screenshots and docs, then ask for tickets, file-level implementation steps and edge cases.

Prompt to try

Turn the uploaded feature brief into an implementation plan. Include tasks, affected files, data model changes, API changes, UI states, tests, and rollout risks.

Avoid these mistakes

Most people use Projects too vaguely.

The fix is simple: keep projects focused, upload the real source material, and ask for source-grounded outputs.

1.Do not upload only one file if the bug depends on imports or config.

2.Do not let AI rewrite a module without explaining the existing pattern first.

3.Do not mix multiple unrelated repos in one project.

4.Do not ignore tests. Ask AI what to run after every change.

5.Do not assume generated code matches your version of a framework.

FAQ

Questions developers usually ask.

Can Projects import files from GitHub?

Yes. Projects can import repository files from GitHub, store metadata such as repo/path/branch information, and index the content for project retrieval.

Can Projects understand an entire codebase?

They work best when you import the files that matter: README, package/config files, core modules, schemas, docs and files related to the task. Retrieval then finds the relevant chunks when you ask.

Can I upload screenshots of UI bugs?

Yes. Supported image files can receive searchable descriptions, which helps keep screenshots and visual references connected to the project.

Which model should coders use inside Projects?

Use comparison. One model may be better at architecture, another at patch style, another at critique. Projects give each model the same code context so the comparison is fair.

Start the right way

Create a project before you ask the hard question.

Upload the material, set the rules, then let MultipleChat retrieve the relevant context for ChatGPT, Claude, Gemini, Grok or AI Collaboration.

Create a project

Start with files and instructions.

Projects overview

See OCR, chunks and integrations.

AI Collaboration

Use multiple models on project context.

See plans

Choose capacity for file-heavy work.