RAG vs Fine-Tuning: Which Should You Choose?
When to use Retrieval-Augmented Generation versus fine-tuning for your LLM application. The answer depends on how your data changes.
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When to use Retrieval-Augmented Generation versus fine-tuning for your LLM application. The answer depends on how your data changes.
After 2 years of microservice complexity for a 4-person team, we moved to a modular monolith and cut deploy time by 70%.
The exact process we use to take a startup from idea to live product in 6 weeks — without cutting quality corners.
We've shipped production apps in both. Here's the honest comparison — performance, ecosystem, developer experience, and when to pick each.
Things nobody tells you about RAG in production — chunking strategies, embedding drift, retrieval quality, and keeping it fast.
You don't need a platform team to do zero-downtime deploys. Here's the exact Kubernetes + GitHub Actions setup we use.
Tell us what you're building. We'll scope it, estimate it, and start within days — not months.
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