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. Chat systems with poor memory management burn tokens on redundant context. 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
- How chat memory, summarization, and escalation choices affect token volume
- Where multimodal workflows create extra latency and cost, and how to design within those constraints
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 101 minutes.
Course modules
10 lessons · 101 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.
Summarization patterns for LLM applications
Choose operational, executive, or structured summaries based on the decision the summary needs to support.
Production chat systems: memory, handoffs, and escalation
Structure chat assistants around session memory, retrieval, containment, and human handoff instead of a single giant prompt.
Multimodal LLM workflows: vision, voice, and cost patterns
Understand where voice and vision help, where they create extra latency and cost, and how to design around those constraints.
LLM-generated features for traditional ML
Use LLMs to generate labels, summaries, and semantic features that feed cheaper, faster downstream models.
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