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.
Frequently asked questions
Answers to the questions teams ask before starting this course.
How do I choose the right LLM for a workload?
Compare candidate models on three axes for your specific workload, not in the abstract: cost per request at your token shape, latency at your concurrency, and output quality against a representative eval set. A premium model is worth its price only when the quality or latency difference changes the product outcome; for many classification and extraction tasks a small model matches it at a fraction of the cost.
When should I use RAG instead of fine-tuning?
Use RAG when the model needs current, changing, or large-volume knowledge — retrieval keeps the source of truth outside the model so it updates without retraining. Use fine-tuning when you need to change behavior, format, or tone rather than inject facts. Full-context (stuffing everything into the prompt) only wins for small, stable knowledge bases where retrieval overhead is not worth it.
Are embeddings cheaper than sending full context?
For anything beyond a small, static corpus, yes. Embedding and retrieving the relevant chunks sends a few hundred to a few thousand tokens per request; stuffing the full corpus into every prompt sends all of it every time, and input tokens are billed on each call. The crossover point is roughly where the corpus stops fitting comfortably and cheaply in the prompt on every request.
Why treat architecture decisions as cost decisions?
Because each architecture choice locks in a cost shape that compounds. Model tier, retrieval strategy, and context size set the per-request token bill, and that bill is multiplied by every request for months. Deciding with cost, quality, and operational overhead visible up front is far cheaper than discovering the cost shape on the invoice later.
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