Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
AI Automation & Agent Engineering
·
Module 1
LLM Foundations for Agent Builders
Transformer LLMs: Token Generation, Latency, Cost, and Quality
Explain how transformer-based LLMs generate tokens and why this affects latency, cost, and output quality.
2
Choosing the Right LLM for Your Agent
Compare the strengths, weaknesses, context windows, and pricing of OpenAI, Anthropic, and Gemini model families for agent use cases.
3
Selecting the Right AI Model: Cost vs. Quality
Choose the appropriate model (reasoning, fast, multimodal, embeddings) for a given agent task based on cost/quality trade-offs.
4
Controlling LLM Output with Temperature, Top-P, and Stop Sequences
Configure temperature, top-p, max tokens, and stop sequences to control determinism and output shape.
5
Structured Output with LLMs
Use structured output (JSON mode / response schemas) to get reliable machine-readable responses from an LLM.
6
Streaming LLM Responses in Node.js
Stream LLM responses token-by-token in a Node.js application using the provider SDK.
7
Managing LLM Costs: Tokens and Dollars
Estimate and monitor token usage and dollar cost for a given LLM call or workflow.
Next module isn't ready yet,
Back to course
Previous module
Next module