About this course
Every LLM architecture pattern has a cost signature. Structured outputs use fewer tokens than free-form prompting. Hybrid search with reranking reduces context size. Agentic workflows multiply API calls. Grounding answers in systems of record avoids expensive retries on wrong answers. This course teaches the production patterns themselves, but always through the lens of cost, latency, and operational overhead — because the architecture you ship today is the cost shape you live with tomorrow.
What you will learn
- How structured outputs reduce token waste compared to free-form prompting
- How retrieval, reranking, and query routing affect the cost of getting context to the model
- Why agentic patterns multiply API calls and how to design around that
- When to ground answers in SQL, tools, or retrieval instead of model memory
- How constrained-choice patterns and LLM-generated features feed cheaper downstream systems
Why this belongs in AI Cost Academy
Production LLM patterns directly shape provider mix, token volume, and infrastructure cost. The architecture decisions you make here — retrieval strategy, agent design, output structure, multi-step workflows — determine your cost trajectory for months.
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 70 minutes.
Course modules
7 lessons · 70 min total read time
Structured outputs for extraction, classification, and scoring
Use schema-constrained outputs for reliable extraction, classification, and decision support instead of brittle free-form prompting.
Hybrid search and reranking patterns for RAG
Combine lexical retrieval, dense retrieval, and reranking so the best evidence reaches the model more consistently.
Query rewriting, decomposition, and retrieval routing
Improve retrieval quality by deciding when to rewrite, split, or reroute queries before they ever hit the retriever.
QA over structured data and grounding patterns
Choose SQL, tool-based grounding, or retrieval when answers need to come from systems of record instead of model memory.
Agentic tool-use patterns: planner, executor, and recovery
Design tool-using systems that can plan, act, retry, and escalate without turning every workflow into an unstable agent.
Binary decisions and constrained choice with LLMs
Use bounded output spaces for routing and approvals without pretending the model should be the final authority.
LLM-generated features for traditional ML
Use LLMs to generate labels, summaries, and semantic features that feed cheaper, faster downstream models.
Frequently asked questions
Answers to the questions teams ask before starting this course.
How does LLM architecture affect cost?
Every production pattern has a cost signature. Structured outputs use fewer tokens than free-form prompting; hybrid search with reranking reduces context size; agentic workflows multiply API calls because each step is its own request; chat systems with poor memory management resend redundant context. The pattern you ship sets your token volume and provider mix for months, so it is a cost decision as much as an engineering one.
Why do agentic workflows cost so much more?
Agents multiply requests. A single user task can become a plan step, several tool calls, observations fed back into context, and a synthesis step — each one a billed request, often with the growing conversation resent as context. Designing for bounded steps, tight context, and early termination is what keeps agentic cost from scaling faster than the value it delivers.
Do structured outputs reduce cost?
Yes, in two ways. Schema-constrained outputs are typically shorter than free-form prose, cutting output tokens, and they reduce retries by making responses reliably parseable instead of occasionally malformed. For extraction, classification, and scoring, structured outputs are usually both cheaper and more robust than free-form prompting.
Does this course teach LLM engineering or just cost?
Both, deliberately. It teaches the production patterns themselves — structured outputs, retrieval and reranking, query routing, grounding, agentic tool use, and constrained decisions — but always through the lens of cost, latency, and operational overhead, because those are the constraints that decide which pattern is right for a given workflow.
More in Cost-aware LLM architecture