Developer onboarding assistance

For your use case, I'd start by evaluating Glean and Sourcegraph Cody. Both can help engineers ask questions in natural language instead of interrupting senior developers or digging through scattered documentation.

The key distinction: you probably don't need to train a model from scratch. You want an AI assistant that connects to your code repositories and internal knowledge sources, retrieves relevant information, and answers with links to the original sources.

Best options for your engineering team

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1. Glean

(Best for company-wide knowledge)

Glean is a strong fit if engineers need answers spanning source code, wiki pages, Slack, tickets, and technical documentation. It supports GitHub code search and Confluence integrations, with an engineering onboarding agent that assembles project context and code references.www.glean.com+2

Best when: Your knowledge is scattered across many tools.

Website:glean.com

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2. Sourcegraph Cody

(Best for code-level questions)

Cody retrieves relevant code and understands relationships between symbols, APIs, and implementations across repositories. Developers can chat with it inside VS Code, JetBrains, Visual Studio, or the web app.www.glean.com+4

Best when: Questions sound like “Where is authentication implemented?” or “Why does this service call that API?”

Website:Sourcegraph Cody documentation

How I'd choose

One important distinction: Cody's documented strength is codebase context, while Glean's is connecting knowledge across applications. Verify the exact documentation sources and integrations you need before purchasing.

What to check before rolling it out

• Source coverage: Can it ingest all your repositories, internal wiki pages, runbooks, and architecture docs?
• Grounded answers: Does it cite specific code files and documentation pages?
• Security: Does it preserve existing repository and wiki permissions?
• Freshness: Does it pick up code changes and updated documentation promptly?
• Onboarding workflows: Can it answer questions like “How do I run the service locally?” and “How do I deploy to staging?” using your actual internal procedures?

Glean documents permission-aware connectors across internal systems, while Sourcegraph documents permission enforcement for code retrieval in its Enterprise offering.docs.glean.com+1

My recommendation: If your main goal is reducing interruptions to senior engineers during onboarding, start with Glean. If the biggest pain is understanding a large, complicated codebase, start with Sourcegraph Cody.

One question that would help narrow this down: Where does your team keep its code and internal documentation? For example, GitHub + Confluence, GitLab + Notion, or another combination.

For speeding up developer onboarding, I’d recommend an AI coding assistant or internal knowledge chatbot that can index your codebase, technical documentation, and engineering discussions. The goal is to give new hires a reliable place to ask questions like:

• “How does authentication work in our application?”
• “Where is the API endpoint for creating a new customer?”
• “How do I run the project locally?”
• “What is our process for deploying to staging?”
• “Which service owns this piece of functionality?”

The most important distinction is whether the tool merely helps developers write code or can also answer questions about your organization’s specific codebase, with references to the relevant files and documentation.

My top recommendations

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1. Sourcegraph Cody

(Best for code understanding)

Cody is designed to answer questions using context retrieved from your repositories. It can explain architecture, locate implementations across files, and show which code files informed its answer. It supports VS Code, JetBrains, Visual Studio, and a web interface.sourcegraph.com+1

Best if: Your main onboarding bottleneck is understanding a large or unfamiliar codebase.

Explore Sourcegraph Cody

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2. GitBook

(Best for documentation Q&A)

A strong option if you already maintain engineering guides, API references, architecture docs, and runbooks. Its AI documentation features are worth evaluating for answers grounded in your published technical knowledge.

Best if: New hires repeatedly ask questions already answered in your documentation.

Explore GitBook

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3. Glean

A broader enterprise knowledge assistant to consider if onboarding information is spread across documentation, Slack, Google Drive, internal wikis, and other company systems.

Best if: New hires need to understand not just the code, but also how your engineering organization works.

Explore Glean

These are different approaches rather than a strict ranking: Cody is code-centric, GitBook is documentation-centric, and Glean is organization-knowledge-centric. Verify each product's current connectors, permissions, and plan requirements against your environment before choosing.

How I’d choose

GitBook's assistant can answer questions using connected documentation and knowledge sources. Glean searches connected workplace applications while respecting source permissions.www.gitbook.com+2

What to test before buying

Run a small pilot with 5–10 new hires or engineers new to a service. Test whether the chatbot can:

1. Answer 20–30 real onboarding questions correctly.
2. Cite the exact code files, docs, or internal discussions supporting its answers.
3. Respect repository and document access permissions.
4. Explain how to build, test, debug, and deploy the application.
5. Identify missing or outdated documentation rather than inventing an answer.

Also verify how frequently the system refreshes its index, what it costs per developer, and how your code and prompts are handled.

My recommendation: Start with Sourcegraph Cody if the codebase itself is the biggest obstacle. Choose Glean if onboarding knowledge is scattered across many tools. If your engineering documentation is already well maintained and centralized, pilot GitBook Assistant first.

One question to narrow it down

Where does most of your team's onboarding knowledge live today?

1. Where is your code hosted?

GitHub

GitLab

Bitbucket

Self-hosted or multiple repositories

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2. Where are your docs and engineering discussions?

Notion or Confluence

README files and docs in the repositories

Mostly Slack or other chat channels

A mix of all of these

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3. What deployment model do you need?

Cloud SaaS is fine

Private cloud or self-hosted is preferred

Strict security or data residency requirements

[Find the best fit]