C1: Python and DSA for AI Systems
2 explainers · 1 interview packs
Learn your GenAI roadmap anywhere, with structure and momentum.
Your complete C1 to C6 library is now app-ready with competency hubs, long-form explainers, and deep interview packs optimized for on-the-go study.
Program Snapshot
16 total docs · 5h read time · 178 interview prompts
C2: ML and DL Fundamentals
1 explainers · 1 interview packs
C3: Transformers and LLM Internals
2 explainers · 1 interview packs
C4: Adaptation and Retrieval
2 explainers · 1 interview packs
C5: Agents, Evals, and Guardrails
2 explainers · 1 interview packs
C6: Production GenAI Engineering
1 explainers · 1 interview packs
Recently Updated Content
Jump directly to high-impact modules.
DSA Patterns for AI Backends
DSA questions in GenAI interviews are now system-shaped: not just "solve this problem," but "design this cache, limiter, scheduler, or workflow graph under real constraints." Strong answers connect complexity analysis to reliability and production operations.
Python for AI Systems (Beyond Syntax)
LLM products succeed or fail on systems engineering around the model: concurrency limits, contract stability, retry discipline, and observability. Most production incidents are Python runtime and integration issues, not core model failures.
Python and DSA for AI Systems Interview Questions
This file targets advanced coding and backend systems interviews where Python engineering and DSA decisions are evaluated together.
Linear Algebra and Optimization for LLM Engineers
Interviewers do not require theorem-heavy derivations for most GenAI roles, but they do require engineering-grade intuition: what geometric signals embeddings carry, how optimization dynamics affect stability, and how to debug learning behavior from curves and metrics.
ML and DL Fundamentals Interview Questions
This file targets advanced math and optimization reasoning needed for LLM and GenAI engineering interviews.
Attention and Transformer Internals
If you cannot explain attention from first principles, you cannot reliably debug transformer behavior, tune serving latency, choose model architecture, or defend tradeoffs in interviews. Modern GenAI roles expect both theory fluency and production intuition.
Tokenization, Context Window, and Cost Engineering
Many GenAI outages and budget overruns are token engineering failures: tokenizer mismatch, context over-packing, and missing token guardrails. Teams that treat token budget as a core systems resource ship faster and cheaper with fewer regressions.
Transformers and Tokenization Interview Questions
This file prepares deep technical interviews on transformer internals, tokenization behavior, and production tradeoffs.
LoRA and QLoRA Practical Guide
Most teams cannot full-fine-tune large models for every use case. PEFT methods, especially LoRA and QLoRA, let you adapt behavior with lower memory and cost while preserving operational flexibility.