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Ananya Iyer
CND-1008 · Machine Learning Engineer · 8.7 yrs · M.S. Artificial Intelligence
Completion
55%
First-try rate
59%
Avg attempts
1.65
Consistency
55%
Curriculum coverage by module
First-attempt mastery signal
Attempts by topic (higher = needs validation)
Strong areas
- • Day 5 — Prompt Engineering Patterns
- • Day 8 — Text Chunking Strategies
- • Day 12 — Metadata Filtering & Hybrid Search
- • Day 15 — Reranking & Context Compression
- • Day 20 — Retrieval Agents
- • Day 21 — Multi-Agent Design Patterns
Needs deeper validation
- • Day 4 — How LLMs Work: Tokens & Context (4 attempts)
- • Day 10 — Retrieval & Matching Engine (4 attempts)
- • Day 7 — Model Selection & Cost Tradeoffs (2 attempts)
- • Day 9 — Embeddings & Semantic Similarity (2 attempts)
- • Day 24 — Human-in-the-Loop Workflows (2 attempts)
- • Day 25 — Evaluation Metrics for LLM Apps (2 attempts)
Unvalidated (skipped)
- • Day 1 — AI Engineering Landscape
- • Day 2 — Python for AI Systems
- • Day 3 — APIs, Async & Streaming Basics
- • Day 6 — Structured Outputs & JSON Schema
- • Day 11 — Vector Databases in Production
- • Day 13 — RAG Architecture Fundamentals
- • Day 14 — Query Rewriting & Expansion
- • Day 16 — Grounding, Citations & Hallucination Control
Skipped missions are explored, not assumed weak.
Recommended interview topics
- Day 4 — How LLMs Work: Tokens & ContextLLM Fundamentals & Prompting
- Day 10 — Retrieval & Matching EngineData, Embeddings & Vector Stores
- Day 7 — Model Selection & Cost TradeoffsLLM Fundamentals & Prompting
- Day 1 — AI Engineering LandscapeFoundations of AI Engineering
- Day 2 — Python for AI SystemsFoundations of AI Engineering
- Day 5 — Prompt Engineering PatternsLLM Fundamentals & Prompting