About this course
Architecture decisions are cost decisions. Whether you choose a larger model or a smaller one, RAG or fine-tuning, embeddings or full context — each choice locks in a cost shape that compounds over time. This course gives you the frameworks and data to make those decisions with clearer economic tradeoffs, not intuition alone.
What you will learn
- How to compare LLMs across cost, latency, and output quality for a specific workload
- How OpenAI and Anthropic pricing models differ and when a premium model is worth the cost
- When to use embeddings vs full context, and the cost implications of each approach
- How to choose between RAG, fine-tuning, and full-context for knowledge-heavy workloads
How to use this course: Work through the modules in order for the full picture, or jump to the lesson that matches the problem in front of you right now. Each module is a standalone read — estimated total time is 47 minutes.
Course modules
5 lessons · 47 min total read time
How to choose the right LLM for your workload
Compare cost, latency, and output quality in a way that supports real engineering and product tradeoffs.
OpenAI vs Anthropic pricing
Use provider and model pricing differences to decide when a premium model is worth the cost.
Embedding model options and tradeoffs
Compare embedding approaches and cost implications when retrieval is part of the production stack.
Embeddings vs full context cost efficiency
Choose when to retrieve vs stuff more context. Decision rules and cost shapes for small vs large knowledge bases.
RAG vs fine-tuning cost tradeoffs
Choose RAG, fine-tuning, or full-context for knowledge-heavy or behavior-heavy workloads with a clear cost and maintenance comparison.
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