How Orbit Uses RAG to Give You Smarter Career Advice

Ask ChatGPT for job search advice. Watch it give you the same answer it gives everyone else.

Update your resume. Network more. Practice your interview skills. The advice is technically correct. It's also completely useless. Because it knows nothing about you. It doesn't know you're a product manager with six years of experience targeting fintech in the Bay Area. It doesn't know you've had three interviews at Company X and are waiting to hear back. It doesn't know your runway is four months and your biggest concern is healthcare coverage.

Generic AI gives generic answers. Your job search isn't generic. That mismatch is the entire problem.

What RAG actually is (no jargon, I promise)

RAG stands for Retrieval-Augmented Generation. In plain language: it makes AI smarter about your specific situation by feeding it your data before it answers.

Without RAG: You ask AI a question. It answers from its training data. Billions of web pages. Zero pages about your life.

With RAG: Before answering, the AI retrieves relevant information from your personal data. Then it generates a response that combines general knowledge with your specific context.

Retrieval makes it personal. Generation makes it useful. That's the whole thing.

How Orbit implements it

When you use Scout Chat, here's what happens behind the curtain:

Step 1: You ask a question

"What companies should I target next?" or "Help me prepare for my interview at Stripe."

Step 2: Context retrieval

Before the AI sees your question, Orbit gathers relevant data:

• Your pipeline. Companies applied to, stages, which ones excited you, which rejected you.
• Your contacts. Who you know at targets, when you last spoke, what they said.
• Your profile. Skills, experience, target role, career preferences.
• Your activity history. Recent applications, interviews, follow-ups.

This uses semantic search powered by vector embeddings. Less technically: Orbit doesn't just match keywords. It understands the meaning of your question and finds the most relevant pieces of your data, even if they use completely different words.

Step 3: Augmented prompt

Your question plus all retrieved context gets sent to the AI as one prompt. The model now has both its general career knowledge and your specific situation.

Step 4: An answer that actually knows you

Instead of "target companies in your industry," it might say: "Based on your pipeline, you've had the most traction with Series B fintech companies. Three matching that profile are hiring for product managers right now." Then it names them. With reasons.

That's a fundamentally different experience.

Why this changes everything

Specificity

Generic advice is ignorable. Specific advice is actionable. When the AI knows your pipeline, it recommends next steps for where you actually are. Not where some hypothetical median job seeker might be.

Pattern recognition

Your data contains patterns you can't see from inside the search. Maybe you get more callbacks from companies under 500 employees. Maybe remote roles convert higher for you. RAG surfaces these patterns and factors them into recommendations automatically.

Time

Without RAG, you'd copy-paste your entire pipeline into ChatGPT every time you wanted contextual advice. With it, context retrieval is automatic. You just ask.

The privacy architecture

Here's where most AI tools get it wrong: they store your data on their servers, process it through their pipelines, sometimes use it for training. Your resume, contacts, salary expectations, sitting on someone else's infrastructure.

Orbit doesn't do this. Your data lives in localStorage and your Supabase account. When RAG retrieves context for Scout Chat, it pulls from your data using your API key. The model processes your prompt and returns a response. Orbit's servers never see, store, or train on your career data.

This is Bring Your Own Key (BYOK). You provide your own OpenAI or Anthropic key in Settings. All AI processing happens through your account. Orbit is the interface, not the intermediary.

What RAG can and can't do

It excels at:

• Connecting dots across your pipeline you'd miss
• Providing specific, data-informed recommendations
• Adapting to your current situation, not a template
• Getting smarter as your pipeline grows

It doesn't replace:

• Your judgment about companies, roles, and people
• Human intuition from live conversations
• Professional coaching for complex life decisions
• The relationship-building that only you can do

The future is AI that actually knows your situation

One-size-fits-all career chatbots are already obsolete. They just don't know it yet. The future is AI that knows your pipeline, your contacts, your goals, your constraints, and navigates the specific terrain in front of you.

That's what Orbit's Scout Chat does today.