Intermediate~3h
LLM Internals: Transformers to Inference in GenAI EngineeringInterview Readiness Check
Transformer architecture from first principles — attention heads, positional encoding, residual streams, MLP layers. How pretraining, SFT, and RLHF shape model behaviour. Inference: batching, KV cache, speculative decoding, quantisation. What every AI engineer must be able to explain in an interview without hand-waving..
transformerattentionKV cachepretrainingSFTRLHFspeculative decodingquantisationinference
Learning path
The GenAI Engineer's Landscape→LLM Internals: Transformers to Inference ← you are here→Advanced Prompt Engineering & System Prompts
What this module tests
- Conceptual understanding of llm internals: transformers to inference fundamentals
- Trade-offs and design decisions in real system scenarios
- transformer and attention — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the LLM Internals: Transformers to Inference check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the LLM Internals: Transformers to Inference check test?
- The check covers transformer architecture from first principles — attention heads, positional encoding, residual streams, mlp layers. how pretraining, sft, and rlhf shape model behaviour. inference: batching, kv cache, speculative decoding, quantisation. what every ai engineer must be able to explain in an interview without hand-waving.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for LLM Internals: Transformers to Inference?
- Key concepts include: transformer, attention, KV cache, pretraining, SFT, RLHF, speculative decoding, quantisation, inference. These frequently appear in genai engineering interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study LLM Internals: Transformers to Inference?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before LLM Internals: Transformers to Inference?
- LLM Internals: Transformers to Inference builds on The GenAI Engineer's Landscape. Complete those first for the best results.
- Is transformer, attention, KV cache covered?
- Yes. The LLM Internals: Transformers to Inference module covers transformer, attention, KV cache among other key concepts. The check includes questions that reflect real genai engineering interview patterns.