AI Champion

Course overview

Build AI agents, ship AI-powered products, and master the tools transforming software development. From your first API call to production-grade multi-agent systems.

11 modules
94 lessons
0 min
Start the course

What you'll build

This isn't a course about theory. Every part ends with a real, working project you built yourself.

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Your own Claude Code

A CLI agent that reads, edits, and searches codebases autonomously.

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Your own Lovable

A web app builder with chat, live preview, and one-click deploy.

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Your own Glean

An AI knowledge search engine that indexes docs and code with citations.

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MCP servers

Universal AI connectors for databases, APIs, and internal tools.

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Multi-agent systems

Orchestrator-worker, router, and pipeline patterns in production.

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A complete SaaS

facelessnaija.com — AI video generation with Paystack and Stripe billing.

How the course works

Short, practical lessons. No fluff. Every lesson is a demo you can follow along with.

01

Watch the demo

Every lesson is under 3:30. Pre-built code walkthroughs — no live coding mistakes, no filler.

02

Build the project

Full source code included. Clone the repo, follow along, and build the same thing yourself.

03

Ship it

Every part ends with a deployable project. By the end, you have a portfolio of real AI tools.

Built for developers who want to lead

Whether you're a backend engineer, frontend developer, or full-stack builder — this course turns you into the AI expert your team relies on.

  • Developers adding AI features to existing products
  • Engineers building AI-native tools and agents
  • Founders shipping AI-powered SaaS products
  • Tech leads pioneering AI adoption at their company
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What's included

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94 video lessons

Short, practical, 4K. Every lesson under 3 minutes 30 seconds.

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Full source code

Clone every project. All code used in the course is yours to keep.

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Discord community

Connect with other AI developers. Get help, share wins, find collaborators.

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Lifetime updates

New lessons and parts added regularly. Buy once, access forever.

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Your instructor

Bahdcoder

10 years of professional software engineering experience building products used by thousands of developers and businesses.

I build AI tools for a living. Not tutorials about AI — actual products that developers use every day. This course is everything I know, distilled into lessons you can finish in under 4 minutes.

LLM VaultUsePortalBridge Agent Runtime
llmvault.dev →useportal.app →

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Part 1

Talk to AI

free

Learn the fundamentals of communicating with AI models through the Anthropic Messages API, from raw curl requests to streaming and structured outputs.

  1. Your first AI message

    Raw curl to the Anthropic Messages API, JSON response breakdown.

  2. How AI models actually work

    Visual: tokens, context windows, probability bars, temperature.

  3. System prompts: giving AI a personality

    Same question, 3 system prompts: code reviewer vs teacher vs pirate.

  4. Multi-turn conversations

    Messages array with history. "AI has no memory — you manage it."

  5. Streaming: real-time responses

    SSE stream in terminal, tokens arriving one by one.

  6. Structured outputs: AI that returns JSON

    Email to structured contact info JSON extraction.

  7. Comparing AI providers

    Same prompt to Claude vs GPT vs Gemini side-by-side.

Part 2

Give AI hands - Tool calls

free

Master tool use and function calling to let AI interact with the real world — fetching data, querying databases, and reading/writing files.

  1. What is tool use?

    Sequence diagram: AI requests, you execute, AI reasons.

  2. Your first tool: fetch GitHub issues

    fetch_github_issues tool hitting real GitHub API on facebook/react.

  3. Multiple tools: GitHub + Google Search

    GitHub Issues + Serper API google search + database query.

  4. The agentic loop

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

  5. Building a real tool: query a database

    AI writes SQL, executes against real SQLite DB, explains results.

  6. Building a real tool: read & write files

    AI reads config, adds a setting, writes back. Seed of Claude Code.

Part 3

Prompt Engineering That Actually Works

free

Master practical prompt engineering techniques including few-shot prompting, chain-of-thought, extended thinking, and systematic prompt evaluation.

  1. Why most prompts fail

    4-step prompt evolution: vague to precise, side-by-side.

  2. Few-shot prompting: teach by example

    Zero-shot vs 3-example extraction, messy emails to clean JSON.

  3. Chain-of-thought: make AI show its work

    "Buy 2 get 1 free" math problem: wrong vs right.

  4. System prompt design patterns

    5-pattern template: Role, Context, Format, Rules, Fallback.

  5. Extended thinking / reasoning mode

    Architecture decision with thinking trace walkthrough.

  6. Prompt testing: how to know if your prompt is good

    10-case eval, prompt change moves accuracy 60% to 90%.

Part 4

MCP — The Universal AI Connector

paid

Learn the Model Context Protocol to build universal AI connectors — from your first MCP server to tools, resources, prompts, and real-world integrations.

  1. What is MCP and why it matters

    Spaghetti integrations to one protocol diagram.

  2. Your first MCP server

    40-line stock price server, Claude Desktop calls it.

  3. MCP tools: let AI call your APIs

    Multi-tool server wrapping a company API.

  4. MCP resources: give AI read access

    Database schemas + API docs as browsable resources.

  5. MCP prompts: reusable templates

    Code review + incident report prompt templates.

  6. Build a real MCP server: database explorer

    PostgreSQL MCP server, natural language querying.

  7. Connect MCP servers to your AI agent

    Part 3 agent using GitHub + DB + Slack MCP servers.

Part 5

Build your own Claude Code

paid

Build a full AI coding agent from scratch with file reading, editing, shell commands, and codebase search — the architecture behind tools like Claude Code.

  1. Architecture of an AI coding agent

    Architecture diagram: input, prompt, loop, tools, output.

  2. The system prompt that makes it a developer

    40-line system prompt walkthrough, behavior comparison.

  3. Tool: Read files with line numbers

    read_file tool schema + execution, reading real Python files.

  4. Tool: Edit files with precision

    Search-and-replace edit_file tool, fixing a real bug.

  5. Tool: Write new files

    AI creates test files with proper structure.

  6. Tool: Run shell commands

    Sandboxed bash with allowed/blocked commands, test runner.

  7. Tool: Search the codebase

    grep + glob tools, finding imports and dependencies.

  8. Putting it all together: the agent loop

    All 5 tools wired in, autonomous bug fix end-to-end.

  9. Streaming the agent's output

    Real-time thinking + acting UX, with vs without streaming.

  10. Context management: don't blow the window

    Context overflow demo, smart reading strategies, token tracking.

  11. Conversation memory & history

    Persistent sessions, multi-turn coding workflows.

  12. Safety: confirming dangerous actions

    Permission model: auto-allow safe, confirm dangerous.

  13. Making it a real CLI

    Argument parsing, colors, spinner, keyboard shortcuts.

  14. Demo: refactor a real codebase

    Session-to-JWT refactoring across multiple files.

Part 6

RAG: Build your own Glean

paid

Build a Glean-style AI knowledge search platform that indexes your company docs, code, and tools — then answers any question with citations.

  1. What is Glean and why build one?

    The enterprise knowledge problem. Glean's $4.6B valuation explained in one diagram.

  2. Embeddings: making documents searchable

    Turn company docs into vectors. Similar questions find similar answers.

  3. Chunking: splitting docs for search

    Fixed vs semantic chunking on real company docs. Quality comparison.

  4. The search index: vector databases

    Store embeddings in pgvector. Build the index your Glean searches against.

  5. Building the search pipeline

    Question to answer with citations. The full RAG pipeline end-to-end.

  6. Hybrid search: vectors + keywords

    Vector search misses ticket IDs. Add keyword search. Quality jumps.

  7. Indexing a codebase

    Index code by function. Ask how auth works, get the right files cited.

  8. Evaluating search quality

    20-question eval. Find the weak spots. Fix chunking. Accuracy jumps 60% to 90%.

Part 7

Build your own Lovable

pro

Create a web-based AI app builder with a chat interface, sandboxed code execution, live preview, and one-click deployment.

  1. Architecture of a web-based AI app builder

    4-system diagram: Chat UI, AI Engine, Sandbox, Preview.

  2. The chat interface for code generation

    Next.js streaming chat UI with code block rendering.

  3. The AI code generation engine

    System prompt + 4 tools (create/update/delete file, run command).

  4. Sandboxed code execution

    WebContainers running Node.js in the browser.

  5. Live preview rendering

    iframe + Vite HMR, split-screen chat + preview.

  6. File system & version control

    Virtual FS, snapshot-based undo/redo, diff viewer.

  7. Iterative refinement: the magic loop

    4-step dashboard build through conversation.

  8. GitHub integration

    OAuth + Git Data API, every AI change = real commit.

  9. One-click deployment

    Vercel API deploy pipeline, live URL returned.

  10. Full demo: idea to deployed app

    RecipeBox app built in 5 prompts, deployed live.

Part 8

Multi-Agent Systems

pro

Design and build multi-agent architectures including orchestrator-worker, router, and pipeline patterns with robust error handling.

  1. Why multi-agent? When one agent fails

    12-tool single agent fails vs 4 specialists succeed.

  2. Pattern: Orchestrator-Worker

    Manager breaks task into research + writing workers.

  3. Pattern: Router / Handoff

    Customer support triage to billing / tech / human.

  4. Pattern: Pipeline

    scraper, cleaner, analyzer, reporter.

  5. Agent communication: message passing

    Direct vs shared state vs event-based comparison.

  6. Human-in-the-loop: when to ask for help

    Email drafter with approve/edit/reject flow.

  7. Error handling across agents

    Retry, fallback agents, circuit breakers.

  8. Build a complete multi-agent system

    4-agent code review: catches SQL injection + exposed keys.

Part 9

Build and ship a SaaS

pro

Build a complete AI-powered SaaS for generating faceless social media videos — from script generation and voiceover to billing and deployment.

  1. The facelessnaija.com idea

    AI SaaS landscape, Africa opportunity, 5 real revenue examples.

  2. Architecture of facelessnaija.com

    5-layer arch: Next.js, AI pipeline, Supabase, billing.

  3. Authentication with Supabase

    Google OAuth + email, protected routes, user profiles.

  4. AI script generator

    Claude generates 60s video scripts with visual directions.

  5. AI voiceover with African accents

    ElevenLabs API, African voice selection, audio storage.

  6. Video assembly pipeline

    Stock footage matching, Remotion composition, MP4 render.

  7. Captions & thumbnails

    Whisper timing, animated captions + AI-generated thumbnails.

  8. Billing with Paystack

    Plans in NGN/GHS/ZAR/KES, checkout, webhooks, subscriptions.

  9. Billing with Stripe

    Stripe Checkout + billing abstraction for Paystack/Stripe.

  10. Usage tracking & rate limiting

    Per-user limits, plan enforcement, cost tracking dashboard.

  11. Prompt caching: cut costs by 90%

    cache_control on system prompt, $5.54 to $0.94 per 1K calls.

  12. Deployment & infrastructure

    Vercel + Supabase production setup, RLS policies, Sentry.

  13. Launch day: analytics & iteration

    PostHog funnel, cost dashboard, 147 signups, 75% margins.

Part 10

AI in production

pro

Ship AI to production with confidence: cost management, evals, guardrails, security, observability, failure handling, and scaling strategies.

  1. The production AI checklist

    Demo vs production comparison, the reference checklist.

  2. Cost management: don't go bankrupt

    Real cost breakdowns, prompt caching, model routing.

  3. Evaluation: testing AI output

    20-case eval suite, LLM-as-judge, 70% to 92% accuracy.

  4. Guardrails: preventing bad outputs

    Input/output/tool guardrails catching real attacks.

  5. Security: prompt injection & data leakage

    5 real attacks + defenses, OWASP Top 10 for LLMs.

  6. Observability: see what your AI is doing

    Langfuse tracing, finding production bugs through traces.

  7. Handling failure gracefully

    Retry, fallback models, circuit breakers, graceful degradation.

  8. Scaling: from 10 users to 10,000

    Architecture changes at each scale milestone.

Part 11

Advanced Agent Patterns

pro

Explore cutting-edge agent patterns including computer use, voice agents, browser automation, document processing, long-running tasks, fine-tuning, and local AI.

  1. Computer use: AI that sees your screen

    Anthropic computer use API, screenshot to action loop.

  2. Voice agents: AI that talks

    STT to Claude to TTS, latency optimization 4.2s to 1.1s.

  3. Browser agents: AI that surfs the web

    Playwright agent scraping Hacker News, summarizing posts.

  4. Document processing: AI that reads anything

    50 invoices (PDF/image/CSV) to structured spreadsheet.

  5. Long-running agents: tasks that take hours

    Job queue, checkpoint/resume, progress dashboard.

  6. Fine-tuning: when prompting isn't enough

    Support ticket classifier: 78% to 94%, 7x cheaper, 6x faster.

  7. Local AI: run models on your machine

    Ollama + Part 3 agent locally, trade-off comparison.