7.2.1 LLM Overview Roadmap: Capability, Cost, Product Fit

LLM overview is not a model-name list. It helps you decide what a large model can do, what it costs, and when prompting, RAG, Agent, or fine-tuning is a better route.

Look at the Capability Stack First

Route	Use when...
prompt	the model already knows enough and task is simple
RAG	private or changing knowledge must be cited
Agent	the model must use tools or take steps
fine-tuning	behavior/style/format needs repeated adaptation

Run One Route Decision

request = {
    "needs_private_docs": True,
    "needs_tool_action": False,
    "needs_repeated_style": False,
}

if request["needs_tool_action"]:
    route = "Agent"
elif request["needs_private_docs"]:
    route = "RAG"
elif request["needs_repeated_style"]:
    route = "fine-tuning"
else:
    route = "prompt"

print("recommended_route:", route)

Expected output:

recommended_route: RAG

This is not a full architecture decision. It is the habit: choose the smallest route that solves the actual product need.

Learn in This Order

Order	Read	What to keep
1	7.2.2 Development History	why scaling and instruction tuning mattered
2	7.2.3 Core Concepts	context, tokens, temperature, latency, cost
3	7.2.4 Industry Landscape	model/provider selection notes
4	7.2.5 LLM Call Workbench	one request/response record

Pass Check

You pass this roadmap when you can explain one model choice in terms of capability, context, cost, latency, data privacy, and route fit.