AI/ML Developer Roadmap

Dive into the world of Generative AI Engineering.

Phase 1: Generative AI Foundations

Understand how LLMs work under the hood.

Tokens, Context Window & Sampling (Temperature)Deterministic vs Probabilistic OutputsCalling LLM APIs (Roles, Tokens, Rate Limits)Prompt Engineering (Few-shot, Chain-of-thought)

Phase 2: Structured Outputs & Validation

Make AI predictable and programmable.

Generating JSON Responses from LLMsSchema Validation with ZodFunction Calling / Tool CallingError Handling (Malformed JSON, Retries)

Phase 3: RAG & Vector Databases

Give AI access to external knowledge.

Understanding Embeddings & Vector SpaceIntegrating Vector Databases (Similarity Search)Building a RAG PipelineStreaming Responses (Progressive Rendering)

Phase 4: AI Agents & LangChain

Build autonomous AI systems.

Understanding AI Agents vs Single LLM CallAgent Design Patterns (Planner, Researcher, Router)Intro to LangChain & LCELBuilding a Multi-Agent Workflow

Phase 5: Evaluation & Aptitude

Test your models and sharpen logic skills.

Evaluating AI Systems (Detecting Hallucinations)Multi-Agent Architecture Concerns (Latency, Cost)Probability and CombinationsPercentage, Ratios, & Problem Solving

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