NavikLab Academy

Master AI engineering across 8 tracks with 102 modules. Powered by Context Engineering Academy.

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Multi-Agent Orchestration

Your weakest dimension is Synthesis. This track will help you improve it.

Context Engineering

Master system prompts, tools, memory, RAG, few-shot examples, and conversation history. The 6 core components and 4 strategies: Write, Select, Compress, Isolate.

Improves: Analytical Rigor82

11 modules

Agent Observability

Tracing, logging, metrics, and dashboards for understanding what your AI agents are actually doing. Tools: LangSmith, Langfuse, Helicone, Phoenix, Weave.

Improves: Metacognitive Depth75

16 modules

LLM Evals

Build evaluation systems that catch regressions before your users do. From simple assertions to LLM-as-judge — evaluate outputs systematically.

Improves: Intellectual Humility85

15 modules

Agentic RAG

Go beyond naive RAG. Learn agentic retrieval patterns — query planning, multi-step reasoning, self-correcting retrieval, and production-grade architectures.

Improves: Creative Range68

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Multi-Agent Orchestration

Design, build, and orchestrate teams of specialized AI agents. Orchestration patterns, communication, shared state, task decomposition, and production deployment.

Improves: Synthesis62

16 modules

Tool Use & MCP

Master tool design for AI agents and the Model Context Protocol. From fundamentals to building MCP servers, security, and production patterns.

Improves: Persistence78

10 modules

AI-Native Development

The vibe coding era: AI IDEs, code generation workflows, AI code review, testing AI output, and building professional AI-assisted development practices.

Improves: Persistence78

8 modules

Model Selection & Cost Engineering

Choose the right model for every task. Intelligent routing, prompt caching economics, batch APIs, open-weight models, and cost attribution for AI at scale.

Improves: Analytical Rigor82