Multiple tools: GitHub + Google Search - AI Champion - AI Champion

Talk to AI

free

Your first AI message
How AI models actually work
System prompts: giving AI a personality
Multi-turn conversations
Streaming: real-time responses
Structured outputs: AI that returns JSON
Comparing AI providers

Give AI hands - Tool calls

free

What is tool use?
Your first tool: fetch GitHub issues
Multiple tools: GitHub + Google Search
The agentic loop
Building a real tool: query a database
Building a real tool: read & write files

Prompt Engineering That Actually Works

free

Why most prompts fail
Few-shot prompting: teach by example
Chain-of-thought: make AI show its work
System prompt design patterns
Extended thinking / reasoning mode
Prompt testing: how to know if your prompt is good

MCP — The Universal AI Connector

paid

What is MCP and why it matters
Your first MCP server
MCP tools: let AI call your APIs
MCP resources: give AI read access
MCP prompts: reusable templates
Build a real MCP server: database explorer
Connect MCP servers to your AI agent

Build your own Claude Code

paid

Architecture of an AI coding agent
The system prompt that makes it a developer
Tool: Read files with line numbers
Tool: Edit files with precision
Tool: Write new files
Tool: Run shell commands
Tool: Search the codebase
Putting it all together: the agent loop
Streaming the agent's output
Context management: don't blow the window
Conversation memory & history
Safety: confirming dangerous actions
Making it a real CLI
Demo: refactor a real codebase

RAG: Build your own Glean

paid

What is Glean and why build one?
Embeddings: making documents searchable
Chunking: splitting docs for search
The search index: vector databases
Building the search pipeline
Hybrid search: vectors + keywords
Indexing a codebase
Evaluating search quality

Build your own Lovable

pro

Architecture of a web-based AI app builder
The chat interface for code generation
The AI code generation engine
Sandboxed code execution
Live preview rendering
File system & version control
Iterative refinement: the magic loop
GitHub integration
One-click deployment
Full demo: idea to deployed app

Multi-Agent Systems

pro

Why multi-agent? When one agent fails
Pattern: Orchestrator-Worker
Pattern: Router / Handoff
Pattern: Pipeline
Agent communication: message passing
Human-in-the-loop: when to ask for help
Error handling across agents
Build a complete multi-agent system

Build and ship a SaaS

pro

The facelessnaija.com idea
Architecture of facelessnaija.com
Authentication with Supabase
AI script generator
AI voiceover with African accents
Video assembly pipeline
Captions & thumbnails
Billing with Paystack
Billing with Stripe
Usage tracking & rate limiting
Prompt caching: cut costs by 90%
Deployment & infrastructure
Launch day: analytics & iteration

AI in production

pro

The production AI checklist
Cost management: don't go bankrupt
Evaluation: testing AI output
Guardrails: preventing bad outputs
Security: prompt injection & data leakage
Observability: see what your AI is doing
Handling failure gracefully
Scaling: from 10 users to 10,000

Advanced Agent Patterns

pro

Computer use: AI that sees your screen
Voice agents: AI that talks
Browser agents: AI that surfs the web
Document processing: AI that reads anything
Long-running agents: tasks that take hours
Fine-tuning: when prompting isn't enough
Local AI: run models on your machine

AI Champion/Give AI hands - Tool calls/Multiple tools: GitHub + Google Search

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The agentic loop

15-line loop: send, tool use, execute, feed back, repeat.

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