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LLM fundamentals

AI Engineer (LLM & Generative AI)

LLM fundamentals questions

18 questions on LLM fundamentals for ai engineer (llm & generative ai)s, easiest first.

Easy 8

What is a tokeneasy
Effect of temperatureeasy
What the context window limitseasy
What a hallucination iseasy
Temperature close to zeroeasy
What top-k sampling doeseasy
What LLM pretraining learnseasy
Handling an HTTP 429 responseeasy

Medium 8

Making prompt caching workmedium
Temperature changes probabilitiesmedium
How many tokens top-p keepsmedium
From logprobs to probabilitymedium
Estimating monthly token costmedium
Savings from a cached prefixmedium
Why add jitter to backoffmedium
Which rate limit binds firstmedium

Hard 2

Temperature 0 is not deterministichard
Budgeting the context windowhard

More AI Engineer (LLM & Generative AI) skills

RAG and vector search 26 AI agents and tool calling 14 LLM evaluation 12 Transformers and LLM internals 11 Prompt engineering 9 Resilience and fault tolerance 7 Model serving and inference 7 LLM safety and guardrails 6 Deep learning and PyTorch 5 Performance and debugging 5 Web application security 5 CI/CD 5 Statistics and probability 4 Machine learning 4 Fine-tuning 4

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