8.3.1 Application Development Roadmap: API, Tools, State

LLM application development is not just an input box plus a model API. A real feature validates input, calls models, uses tools, keeps state, parses output, logs errors, and gives users a recoverable experience.

See the Application Loop First

The chapter upgrades one model call into a maintainable application loop: input, prompt/context, model, optional tool, validation, output, feedback.

Run a Tool Dispatch Check

Function Calling means the model proposes structured action arguments, but your application must validate and dispatch them.

model_output = {
    "tool": "search_docs",
    "arguments": {"query": "RAG citations"},
}

allowed_tools = {
    "search_docs": {"required": ["query"]},
    "create_ticket": {"required": ["title", "priority"]},
}

tool = model_output["tool"]
required = allowed_tools[tool]["required"]
validation_ok = all(name in model_output["arguments"] for name in required)

print("validation_ok:", validation_ok)
print("dispatch:", tool if validation_ok else "block")

Expected output:

validation_ok: True
dispatch: search_docs

Never execute tool calls directly from model text. Validate tool name, argument schema, permission, and failure path.

Learn in This Order

Step	Read	Practice Output
1	LLM API practice	Write a robust call wrapper with timeout and error handling
2	Framework basics	Split prompt, model, tool, memory, retrieval, and parser roles
3	Function Calling	Validate structured tool arguments before dispatch
4	Hugging Face ecosystem	Know when hosted, local, or browser-side models fit
5	Dialogue systems	Store session state, slots, memory, and user feedback
6	Document and template apps	Turn parsing, extraction, and generation into modules

Pass Check

You pass this chapter when you can build a small assistant loop that handles one API call, one optional tool call, one structured output, and one error path.

The exit mini project is a course Q&A and study-planning assistant that classifies the user request, optionally retrieves knowledge, returns structured suggestions, and logs feedback.