
RAG vs Fine-Tuning vs Prompting
The three main ways to adapt an LLM to your business—prompting, retrieval-augmented generation, and fine-tuning—compared on cost, freshness, accuracy, and when each one is actually the right call.
Practical perspectives on enterprise software development, infrastructure automation, and cloud engineering from our project experience.

The three main ways to adapt an LLM to your business—prompting, retrieval-augmented generation, and fine-tuning—compared on cost, freshness, accuracy, and when each one is actually the right call.

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Elasticsearch changed its license. AWS forked OpenSearch. Now you have to choose—here's what actually matters for your search and analytics workloads.

CloudFront integrates deeply with AWS. Cloudflare offers a generous free tier and broader edge platform. Here's how to choose your CDN.

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When to use a hosted LLM API like OpenAI or Anthropic versus running an open-weight model yourself—compared on cost, privacy, control, and the operational burden teams underestimate.

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Datadog is a polished all-in-one platform. The Grafana stack (Prometheus, Loki, Tempo) gives you control and avoids vendor lock-in. Here's how to choose.

Terraform provisions infrastructure. Ansible configures it. They solve different problems but the overlap creates real confusion—here's how to think about it.
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