Building AI Applications

Why Smarter. An AI application is more than a model: it is a model plus instructions, knowledge, tools, guardrails and, increasingly, other models. Most courses can show these parts only one at a time, in a different framework each, and only to students who can program. In Smarter, each part is a resource declared in a short YAML manifest, with the same apply, describe and delete lifecycle: Vectorstores on Qdrant or Pinecone for retrieval, SQL and API plugins over a Connection for structured data, MCP clients for the tools of any MCP server, Guardrails for safety, and an Orchestrator for multi-agent workflows. A course can cover the whole landscape of AI application architecture in a term, without first teaching a programming language.

Students learn from complete, working applications, not diagrams. Every installation ships with nineteen built-in LLMClients to take apart, such as research_analyst, which searches the web through the Exa MCP server, reads pages and PDFs, and checks its citations with an output guardrail; twenty-five ready-made MCP clients for services such as GitHub, Notion, Linear and Stripe; twenty sample plugins; and Stackademy, a fictional university whose course catalog is served both from a database and from a REST API.

Smarter also makes the design decisions behind safe AI applications visible, where students can see them and argue about them:

  • A tool is used only after two decisions: Smarter selects a plugin when the prompt matches its search terms, and only then does the model decide whether to call it.

  • A SQL Plugin runs a fixed, parameterized query that the model can only fill in, so the model never writes SQL that runs. The data_analyst LLMClient shows the other pattern: a skill that teaches the model a schema, so that it writes SQL for a person to review.

  • Credentials are Secrets, named in a manifest but never shown to the model, and MCP connections are restricted to public https endpoints, with an allow-list of the tools the model may use.

  • An Orchestrator coordinates several LLMClients with one of six strategies: sequential, parallel, supervisor, router, voting and debate.

  • Every request is charged with its tokens, so students can compare one assistant with a workflow of several by cost and latency, as well as quality.

What the course covers. The anatomy of an AI application, and how each part appears as a Smarter resource; models and prompts for applications, including structured output; retrieval: embeddings, chunking, top-k, citations and measuring retrieval quality; structured data, and the safe patterns for letting a model answer from a database; tools, function calling and plugin selection; the Model Context Protocol, and trusting an MCP server; tool safety and prompt injection through retrieved content; orchestration, from workflows to agents; evaluation with test sets, rubrics and LLM judges; and the production concerns of guardrails, budgets, cost accounting, the journal and deployment.

Look at.

Each course plan lists every manifest and file that it uses under Smarter in the Source Code.

Read. Smarter Vectorstore, Smarter Vectorsearch, Smarter MCP Client, Smarter Connection, SQL Plugin, Smarter Orchestrator, Natural Language Processing (NLP).

Exercises.

  • Take the research_analyst manifest apart: list every resource it uses, what each does, and draw how a question flows through it. Confirm each step in the workbench’s Console.

  • Load a course syllabus and handbook into a Vectorstore, and build a teaching assistant that answers questions about them, with citations.

  • Answer business questions in plain English over the Stackademy database, with the stackademy_sql SQL Plugin, and compare it with the data_analyst LLMClient, which writes SQL for a person to review. Which would you let a manager rely on, and why?

  • Build the same workflow as a single LLMClient and as an Orchestrator of several, and compare cost, latency and quality.

Course plans. Three complete, ready-to-teach plans, each with a schedule, graded assignments and rubrics, two capstone projects, recommended texts, further study and technical prerequisites. See Course Formats and Grading for what the formats have in common.

See the Smarter Instructor Guide to install Smarter and set up a class.