AI & Agents Engineer
F · LLM fundamentals and AI engineering
16 questions
16 questions
Learning paths
- RAG and privacy · 8 steps
- Agents and control over actions · 7 steps
Questions
- F-P01What is an LLM?Basic
- F-P02What is a prompt, and how does an instruction differ from data?Basic
- F-P03What are a token and a context window?BasicSTART 30
- F-P04What does inference mean, and what determines a model's response time?Basic
- F-P05What is temperature for in a language model?Basic
- F-P06What are structured outputs, i.e. answers conforming to a schema?Basic
- F01Why can a model convincingly give an untrue answer?Basic
- F02How do you manage a model's limited context?Intermediate
- F03How does asking for JSON differ from a schema-enforced response?Intermediate
- F04How does a model call tools?Intermediate
- F05Does a temperature of zero give an identical result every time?Intermediate
- F07How do you improve a prompt without tailoring it to a few known examples?Intermediate
- F08When do you choose RAG, fine-tuning or plain code?Intermediate
- F09Does high vector similarity mean a document contains the true answer?Basic
- F10What does streaming a model's answer actually improve?Advanced
- F06How do you choose a model and assess the cost of a whole task?Advanced