How to Onboard onto an Unfamiliar Codebase Using AI?

Joining a new codebase is disorienting. The folder structure is unfamiliar, the naming conventions are cryptic, the architecture docs are missing, and the one developer who knows everything is always in a meeting.

AI will not read the codebase for you, but it significantly shortens the time between landing in a repository and understanding how it works. This guide covers each stage of codebase onboarding and how to use AI at every step.

Step-by-Step Guide to Onboarding an Unfamiliar Codebase with AI

Understanding a codebase is gradual. The steps below follow the natural order of that process, starting with the big picture and working inward. At each stage, AI helps you move faster without skipping the work.

1. Map the Codebase Structure

The first thing to do when you land in an unfamiliar repo is to understand what you are looking at before trying to understand how any of it works. Start with the top-level folder structure and the obvious configuration files.

Before diving into any code, it helps to research the frameworks and architectural patterns you encounter. Paste the folder structure and any config files into AI and ask it to explain what each directory likely contains and how the pieces relate to each other. You get a working hypothesis about the architecture before reading a single file.

What to look at first:

• Package manifest files like package.json, go.mod, requirements.txt, or Cargo.toml for dependencies and build scripts
• Entry points like main.*, index.*, app.*, or server.* for where the application starts
• Config files like docker-compose.yml, vite.config.*, or angular.json for how the project runs

For researching unfamiliar framework conventions or architectural patterns you spot in the folder structure, Chatly's AI search engine gives you a direct answer without trawling through documentation.

2. Identify Core Technologies

Once you know the structure, identify exactly what the project is built with. This is more than just the programming language. It includes the frameworks, databases, build tools, testing libraries, and any major third-party services.

Paste the dependency files and the imports from the main entry-point files into AI, and ask for a plain-English summary of the tech stack. Ask what each major dependency is for and how the pieces fit together. This saves the time you would otherwise spend googling each unfamiliar library one by one.

A useful prompt to run here:

"Based on these dependency files, summarise the tech stack in plain English. What is each major library responsible for, and are there any patterns or conventions I should know before reading the application code?"

For quickly summarising dense setup docs, changelogs, or README files that describe the stack, the summary generator condenses long text into the key points you actually need.

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3. Understand the Application Flow

Understanding the structure is different from understanding what the application actually does when it runs. Application flow means tracing how a request or user action moves through the system from start to finish, through the frontend, the backend, the service layer, and down to the database.

Pick one simple flow to trace first. A login, a page load, or a basic API call are good starting points because they touch most of the layers without being too complex.

How to use AI here:

• Find the relevant route, controller, or handler for your chosen flow
• Paste the files involved and ask AI to explain the sequence of what happens when that request comes in
• Ask it to identify what each layer does and what it passes on to the next

The Explain AI feature is particularly useful for this, breaking down what a block of code is doing step by step in plain language rather than producing more code output.

4. Summarise Large or Complex Files

Every codebase has genuinely intimidating files:

• A 2,000-line service class
• A utility module with 40 functions
• A database abstraction layer that has grown for years

Reading these before you understand the broader context is slow and rarely productive.

Paste a large file into AI and ask for a summary before reading it in detail. A good summary tells you what the file is responsible for, which parts are most important to understand first, and whether there are any patterns it relies on that are not obvious from reading it.

What to ask when summarising a complex file:

• What are this file's main responsibilities?
• Which functions are called most frequently from elsewhere?
• Are there any non-obvious patterns or conventions being used here?
• What would break if this file were removed or significantly changed?

5. Trace Key Features End to End

Once you have a broad picture of the system, trace specific features all the way through the stack. Pick the features that matter most to the product: login, payments, notifications, and the main user action the product is built around.

How AI helps here:

• Paste the files involved in a feature and ask AI to map the data flow from entry to exit
• Ask it to identify where one layer hands off to the next and what each function is responsible for
• Ask what could go wrong at each point in the chain

Use the AI coder app for features that span multiple files and services, since it handles multi-file context better than a basic chat session and is designed for exactly this kind of code analysis work.

6. Decode Naming Conventions and Patterns

Every codebase has an internal vocabulary that is invisible to outsiders. Folders called handlers, resolvers, presenters, or facades mean different things in different projects. Abbreviations like txn, ctx, svc, or repo are used consistently within a team but never explained anywhere. Design patterns get applied differently across codebases.

When you encounter naming or patterns you do not recognise, paste examples into AI and ask what the naming convention likely means in this context, what design pattern is being applied, and whether there are well-known conventions the codebase seems to be following.

Keep a running note of the terms and patterns you decode as you go. This personal glossary makes the second week significantly easier than the first.

7. Locate Important Business Logic

Business logic rarely lives where you expect it to. What started as a validation in the controller migrates over time to a service, then to a utility, and then gets split across both. Finding where the critical rules, calculations, and workflows actually live is one of the most important things to do early.

AI can help you find it faster. Describe the business behaviour you are looking for and ask AI to identify which parts of the code you have already read are likely to contain it, or where in the structure it would typically live based on the project's conventions.

Useful prompts for locating business logic:

• "Based on the code I have shared, where is the validation logic for user permissions likely to live?"
• "Is this implementation the authoritative business rule, or does it look like a wrapper around something else?"
• "Explain this rule in plain English: what business decision does this code encode?"

For targeted questions like these during exploration, the Ask AI app is well-suited to quick, focused queries without needing to set up a longer session.

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8. Review Configuration and Environment Setup

Getting the application running locally rarely goes smoothly. Environment files, secrets management, build configuration, and local setup scripts are among the most project-specific and least documented parts of any codebase.

AI can help you make sense of these faster. Paste the .env example, docker-compose.yml, Makefile, or set up a README and ask for a plain-English explanation of what each variable or step does, what the setup process is actually doing, and what is most likely to go wrong.

What to ask during environment setup:

• What does each environment variable control, and what happens if it is missing?
• What does this Docker or build configuration set up, and in what order?
• What are the most common setup failures for a project like this?

If the setup documentation lives in a PDF, an internal wiki export, or any uploaded document, the Chat PDF feature allows you to ask direct questions about the setup process rather than reading the whole document to find the relevant section.

Recommended read: Why PDFs Are Hard to Read, Search, and Understand

9. Analyse Dependencies and Third-Party Integrations

Modern applications are as much a collection of integrations as they are original code. Payment providers, notification services, analytics platforms, message queues, feature flags, and external APIs all need to be understood to have a complete picture of how the system works.

Paste the integration files, wrapper classes, or SDK configuration into AI and ask what each integration does, how it is being used, and what the data flow looks like between the application and the external service.

Questions worth asking about each integration:

• What does this service do, and why is it used here rather than an alternative?
• What data is being sent to this service, and what comes back?
• What would break if this integration were temporarily unavailable?

10. Understand the Database Schema

The database schema is one of the most information-dense parts of any application. Models, migrations, relationships, and indexes together tell you what data the application works with, how it is structured, and how different entities relate to each other.

Start with the migration files or model definitions and ask AI to produce a plain-English summary of the data model. Ask what each table contains, how the key relationships work, and which entities are central to the product's core functionality.

Useful prompts for schema understanding:

• "Summarise this data model. What are the core entities and how do they relate?"
• "What does this migration do, and why would it have been needed?"
• "Are there any non-obvious schema patterns here, such as soft deletes, polymorphic relationships, or denormalised fields?"

11. Use AI for Debugging While Learning

One of the best ways to learn how a system actually behaves is to debug something in it. Reading code tells you what the system is supposed to do. Debugging tells you what it actually does, where assumptions break down, and what the real data flow looks like under live conditions.

When you encounter an error, do not just try to fix it immediately. Use the debugging process as a learning exercise. Paste the stack trace, the relevant code, and a description of what you were doing, then ask AI to explain what the error means, where in the system it is coming from, and what it reveals about how that part of the codebase works.

How to get more from debugging with AI:

• Ask for an explanation of the cause before asking for a fix
• Ask what the error tells you about the system's behaviour, not just how to resolve it
• Ask whether this type of error is likely to appear elsewhere in the codebase, given the same pattern

Using Chatly's AI coder for this kind of diagnostic work gives you an explanation and a fix analysis in the same place, which is more useful during onboarding than a pure fix suggestion, because understanding the cause is the actual goal.

For more details, read: AI for Developers: Code Review, Debugging, Documentation, and Integrated Workflows

12. Generate Personal Documentation

Most codebases are underdocumented, and the documentation that does exist is written by people who already know the system well enough to skip the parts that seem obvious to them. The most useful documentation you can create early in your onboarding is documentation written for yourself.

As you work through the codebase, use AI to help generate personal notes: architecture summaries in your own words, a glossary of internal terms, a map of how key features flow through the system, and a personal setup guide that captures the steps that were missing from the official docs.

Documentation worth creating during onboarding:

• An architecture overview in plain language with your own annotations
• A glossary of internal terms, abbreviations, and patterns
• A map of two or three key features traced end to end
• A personal setup guide with the actual steps that worked, not the ones in the README

The AI document generator converts raw notes and code explanations into structured, readable documentation that is also worth sharing with the team.

Recommended read: How to Use AI to Write Technical Documentation Your Team Will Actually Read

13. Prepare Better Questions for Teammates

One of the most underappreciated skills in onboarding is knowing how to ask a good question. Asking "Can you explain how authentication works?" is a different burden on a teammate than asking, "I can see the JWT is validated in auth.middleware.ts and the user is fetched in user.service.ts, but I am not sure where the session is persisted between requests. Is that handled in the middleware or somewhere further downstream?"

The second question shows you have done the work, makes it easy for the teammate to give a precise answer, and gets you further faster.

Before asking a teammate anything, spend time with AI first. Get as far as you can independently, build a clear picture of what you do and do not understand, and formulate a question that can be answered specifically. This respects your teammates' time and produces better answers because you already understand the context around the question before you receive the reply.

14. Always Verify AI Insights Against the Real Code

This is the most important step and the one most likely to be skipped when AI explanations feel confident and clear. AI works from what you paste into it and can produce plausible-sounding explanations that are subtly wrong, especially for complex business logic, custom framework behaviour, or code that behaves counterintuitively.

Every explanation AI gives you should be treated as a hypothesis rather than a conclusion. Verify it against the actual code, follow the logic yourself, run the code locally, and check that the behaviour matches what AI described.

Signs an AI explanation may need closer checking:

• The explanation sounds too clean for a visibly messy codebase
• Edge cases or special handling are not mentioned
• The explanation contradicts something you observed when running the code
• The AI hedges with phrases like "this likely" or "this probably"

The combination of AI for fast orientation and manual verification for accuracy is more effective than either alone.

15. Build a Continuous Learning Workflow

Onboarding is not a phase that ends after the first two weeks. Understanding a codebase deeply takes months, and the approach that works is not a one-time effort but a repeating loop.

A practical rhythm looks like this:

1. Explore manually — pick a part of the system you do not yet understand and read it yourself
2. Ask AI — use AI to fill in gaps, explain what you missed, and answer specific questions
3. Validate — verify both against real behaviour by running code or reading tests
4. Document — add what you learned to your personal notes
5. Repeat — pick the next unfamiliar part and run the loop again

For more complex multi-step tasks, like asking AI to analyse a set of related files, generate a summary, and produce onboarding notes in a single workflow, the AI agent feature handles sequences of tasks rather than single-shot requests.

For a broader look at how AI is changing the day-to-day experience of development work, the vibe coding overview covers that shift well.

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Conclusion

Getting up to speed on an unfamiliar codebase is genuinely hard in proportion to how large and old the system is. AI does not make it trivial, but it compresses every individual step: understanding the structure, tracing features, decoding logic, finding where things live, and building the mental model that lets you contribute confidently.

The key is using AI to amplify your own exploration rather than replace it. Read the code yourself, use AI to explain what you find, verify what AI tells you, and document what you learn. Run that loop consistently, and the codebase stops feeling foreign faster than you would expect.