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.

1:1 coaching 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.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.