RAG vs Fine-Tuning
Know When to Use Each.

RAG (Retrieval-Augmented Generation) gives the model access to external documents at query time. It's like giving someone a reference book to look things up. Fine-tuning changes the model's weights through additional training. It's like teaching someone new skills they'll remember forever. RAG adds knowledge; fine-tuning changes behavior.

Use RAG when: 1) Your knowledge base changes frequently and you need up-to-date information, 2) You need citations and source attribution, 3) You're working with proprietary documents the model was never trained on, 4) You want to avoid the cost and complexity of training. RAG is ideal for Q&A over company docs, customer support bots, and research assistants.

Use fine-tuning when: 1) You need consistent style, tone, or formatting the base model doesn't naturally produce, 2) You're teaching domain-specific reasoning or classification patterns, 3) You need faster inference without retrieval latency, 4) The knowledge is relatively static and doesn't need frequent updates. Fine-tuning excels at specialized classification, consistent brand voice, and domain-specific reasoning.

Yes, and this is often the best approach for complex applications. Fine-tune for behavior (style, reasoning patterns, output format) and use RAG for dynamic knowledge. For example, fine-tune a model to respond in your brand voice and follow your output schema, then use RAG to inject product information and customer context at query time.

It depends on scale and use case. RAG has higher per-query costs (embedding + retrieval + longer prompts) but zero training costs. Fine-tuning has upfront training costs but lower per-query costs and no retrieval infrastructure. For low-volume applications, RAG is usually cheaper. At high scale with stable requirements, fine-tuning often wins. The real cost is building the wrong thing first.

The cohort gives you a senior AI engineer's perspective on your specific use case. Instead of reading generic blog posts, you get direct guidance: 'For your customer support bot with 50K monthly queries and frequently changing product info, here's exactly what I'd build.' You skip the trial-and-error phase that costs most teams months of wasted effort.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.