LLMs
Large language models — APIs, prompting, fine-tuning, and evaluation.
Maturity level: L2 — Can Build
Six perspectives on LLMs
Roadmap
Core after Python for GenAI, LLMOps, and agent paths.
Architecture
Central inference component in GenAI system design.
Company
Universal expectation in GenAI and LLMOps roles.
Projects
Every GenAI capstone involves LLM integration.
Interview
Deep on evaluation, cost, and production failure modes.
Career
Required for LLMOps, GenAI, and Agentic AI careers.
What & Why
What: Large language models accessed via API or self-hosted for text generation and reasoning.
Why: Foundation of modern GenAI, RAG, agents, and LLMOps systems.
Build this
LLM application with evaluation harness and cost monitoring.
Production reality
- ! Hallucinations
- ! Rate limits
- ! Cost spikes
- ! Latency
- ! Provider outages
Interview preparation
- Prompt vs fine-tune vs RAG
- Evaluation strategies
- Cost optimization
Failure scenario: LLM Provider Rate Limit
- Symptom
- 429 errors spike, latency increases
- Root cause
- Burst traffic exceeds provider quota without backoff
- Permanent fix
- LLM gateway with rate limiting, caching, and multi-provider routing
Connected skills
Explore LLMs in the interactive universe or train with live cohorts.
Want this LLMs learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →