Learning Path Planning
Skill-gap diagnosis → three-stage roadmap → hands-on projects. Actionable and trackable.
Current match
42%vs target role
Target role
LLM Engineer
$140-280k · Application · demand 95
Learning progress0/12 · 0%
Tip: complete the career diagnosis to see your real match and skill gaps against the target role.
Career learning paths
Choose a track based on your background and target impact.
AI Tool User to Power User
20-30% productivity boost→ AI-Enhanced Professional
1-2 months · 30-50 hours
ChatGPT/ClaudePrompt EngineeringAI Tool Integration
Developer to AI Application Builder
40-60% salary increase→ AI Application Developer
3-6 months · 150-200 hours
PythonLLM APIsRAGLangChainAgent Development
Engineer to ML Engineer
50-80% salary increase→ Machine Learning Engineer
6-12 months · 300-500 hours
ML FundamentalsDeep LearningMLOpsProduction Systems
Researcher to AI Scientist
60-100% salary increase→ AI Research Scientist
12-24 months · 800-1200 hours
Advanced MLResearch MethodsNovel AlgorithmsPublications
Three-stage roadmap
1
Stage 1 · 1 - 2 months
Master the Toolchain · Build Something with AI
Master LLM APIs, prompt engineering and the core toolchain — ship your first AI app.
Curriculum (tap to check off)
Recommended resources
- CoursePrompt Engineering Guide
- ProjectChatbot Hands-on
- Open SourceLangChain / AI SDK Docs
🏁 Milestone: Independently deliver a demoable AI app prototype
2
Stage 2 · 2 - 3 months
Engineering · Make the AI System Robust
Engineer the AI app into a stable, evaluable, production-ready system.
Curriculum (tap to check off)
Recommended resources
- CourseRAG Systems in Practice
- ProjectEnterprise Knowledge-base Q&A
- PaperRetrieval-Augmented Generation Survey
🏁 Milestone: Ship a RAG knowledge-base system with evaluation metrics
3
Stage 3 · 1 - 3 months
AI Agents & Multi-Agent Systems
Build agents that plan and collaborate autonomously — enter the high-value track.
Curriculum (tap to check off)
Recommended resources
- CourseMulti-Agent System Design
- ProjectRecruiting Agent
- PaperReAct / Reflexion Papers
🏁 Milestone: Deliver an agent product that completes task chains autonomously
AI skill map
123+ skills across 9 categories. Tap to expand.
Project library
Learn by building, from beginner to advanced.
RAG Knowledge-base Q&A
BeginnerBuild a queryable knowledge base from documents — master the full RAG pipeline.
PythonVector DBLLM API
AI Code Review Assistant
IntermediateAuto-analyze PRs and give review comments — practice prompt engineering and tool calling.
AgentGitPrompt Engineering
Recruiting Agent
AdvancedA multi-agent system that autonomously screens resumes, matches and runs first interviews.
LangGraphMulti-agentWorkflow
Multimodal Document Parsing
IntermediateParse mixed text-image documents into structured output — combine multimodal and OCR.
OpenCVMultimodalLLM
Staying current
AI skills go stale in 12-18 months — a sustainable system to keep up.
Metadata
Title: Continuous Learning for AI Professionals
Last Updated: 2026-07-15T20:16:02.492347
Staying Current
Strategies
Goal: Stay aware of what's happening
Sources
- ›Hacker News (https://news.ycombinator.com)
- ›r/MachineLearning (papers, discussions)
- ›AI newsletters (TLDR AI, The Batch)
- ›Twitter/X (follow key researchers)
- ›arXiv (new papers daily)
Strategy: Daily Reading (30-60 min/day)
Goal: Develop depth in areas
Strategy: Weekly Deep Dive (2-3 hours/week)
Activities
- ›Read 1 important paper thoroughly
- ›Watch technical talk/lecture
- ›Try new library/tool
- ›Write blog post explaining concept
Goal: Hands-on practice
Strategy: Monthly Project (5-10 hours/month)
Activities
- ›Implement recent paper
- ›Build toy project with new tool
- ›Contribute to open source
- ›Participate in Kaggle competition
Goal: Structured skill development
Strategy: Quarterly Skill Refresh
Activities
- ›Take short course on new topic
- ›Attend conference (NeurIPS, ICML, local meetups)
- ›Review and update resume/portfolio
- ›Assess gaps, plan next quarter
Why Critical: AI moves fast - skills become outdated in 12-18 months without continuous learning
Time Commitment: 5-10 hours/week recommended
Avoiding Tutorial Hell
- ›Limit: 1-2 courses max at a time
- ›Rule: For every hour of video, spend 2 hours building
- ›Test: Can you build something from scratch?
- ›Shift: From consumption to creation
Learning Priorities By Role
Ml Engineer
Can Ignore
- ›Pure theory papers (unless research-heavy role)
- ›Every new model (focus on patterns)
Nice To Know
- ›Latest research papers
- ›Adjacent domains (LLMs if you do CV)
Must Stay Current
- ›New frameworks/libraries (PyTorch updates, new tools)
- ›MLOps best practices
- ›Production patterns
Llm Engineer
Can Ignore
- ›Non-LLM papers
- ›Hardware/infrastructure (unless that's your job)
Nice To Know
- ›Transformer internals
- ›Training methods (RLHF, DPO)
Must Stay Current
- ›New LLM releases (GPT-5, Claude 4, etc.)
- ›Prompt engineering techniques
- ›RAG/fine-tuning best practices
- ›Agent frameworks
Ai Researcher
Can Ignore
- ›Production concerns (unless shipping)
Nice To Know
- ›Industry applications
- ›Engineering best practices
Must Stay Current
- ›Papers in your subfield (read everything)
- ›Adjacent subfields (inspiration)
- ›Trending research directions
Efficient Learning Techniques
Paper Reading
- ›Read abstract → conclusion → figures → skim → deep read (in that order)
- ›Focus on papers with code (easier to understand)
- ›Implement key ideas (best way to learn)
- ›Join paper reading groups (discuss with others)
Skill Acquisition
- ›Learn by building (not just watching tutorials)
- ›Teach others (best way to solidify)
- ›Spaced repetition (revisit concepts)
- ›Focus on fundamentals (trends change, fundamentals stay)
🎤
Interview prep
Technical topics, coding, behavioral and system design.
Start prepping →📚
Resource library
Courses, certifications, tools, papers, media and communities.
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