AI Cost Academy
Build production LLM applications
Choose the right LLM pattern for structured data, retrieval, agents, chat, multimodal workflows, and ML-adjacent systems.
Course goal
Choose and implement the right LLM pattern for one production workflow.
Built for application engineers, ml engineers, product builders. Work through the modules in order if you want the full picture, or jump directly to the lesson that matches the job in front of you right now.
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.