Gemini FDE Certify
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Deploy AI. Prove it.

Master the tools. Build real projects. Earn certification that matters in a gamified environment built for engineers.

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How it works

Learn → Build → Deploy

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Learn by doing

Short, focused lessons that drop you straight into a real Gemini codebase.

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Build real projects

Ship agents, RAG pipelines, and evals you can put straight into a portfolio.

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Deploy & prove it

Earn verifiable certification your future employer can actually check.

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See inside before you commit.

Open any module and preview its first lesson free. Unlock the full path — projects, boss battles, and your verifiable badge — when you're ready.

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MODULE 01
The Prompt Forge
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memory
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MODULE 02
Gemini API & Context
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MODULE 03
Advanced RAG Architect
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Gemini FDE Certify
14
2,450 XP
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Good morning, Alex.

day 14! 🔥

You're 2 lessons from Level 25. Keep the streak alive.

14-day streak
2,450 XP
Ruby · Rank #2
LEVEL 24
80% to Level 25
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Continue Learning
Module 4

Function Calling with Gemini

Master the art of equipping Gemini models with external tools to fetch real-time data.

LESSON 4 OF 6 66% · ~12 min left
Certification Pathway
2 / 6 complete
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Alex Rivera

SENIOR AI ENGINEER location_onSan Francisco, CA

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All 6 modules · unlimited challenges · cert exam included

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Alex Rivera
alex.rivera@gmail.com
Level 24 · Senior
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$49 / year · renews Jul 14, 2026

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The FDE Blog

Deep dives, platform updates, and field notes. Some posts link straight into the roadmap.

★ NEW DROP ★FDE BLOG
All Technical Platform Tips
★ Featured Technical · Deep dive

Context Rot: why more context can make your model worse

Bigger windows feel like free memory, but accuracy decays as input grows — even on trivial tasks. The evidence across 18 models, plus what to do about it.

schedule9 min hubLinked to Module 2 Read articlearrow_forward
More posts
Platform update Coming soon

Boss Battles now adapt to your skill level

Difficulty now scales with your recent accuracy so every fight stays in the challenge zone.

4 min · Product
Tips hubModule 1

5 prompt patterns every FDE should keep on hand

Few-shot framing, role priming, and three more reusable structures with copy-paste templates.

6 min · Tips · Coming soon
Technical · Agents hubModule 5

The agentic loop & hub-and-spoke orchestration

How the loop terminates on stop_reason, and why a coordinator delegating to isolated subagents beats a flat swarm.

8 min · TechnicalReadarrow_forward
Changelog Coming soon

Ruby league, streak freezes & more

This month's release notes: a new competitive league, streak protection, and faster boss loading.

3 min · Product

The FDE Roadmap

Follow the spine top to bottom. Modules fan out into lessons — and key lessons branch again into deep-dive side-tracks. Beat each boss to unlock what's next.

1 of 6 done! 🎉
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★
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ONE-TIME UNLOCK
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1 year access • Free updates

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EXERCISES
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HOURS
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LESSONS

What's included

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Curriculum

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Gemini FDE Certify

| Data Pipeline Optimization
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star250 XP
local_fire_departmentPerformance Module
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{{ exStepTitle }}

When building RAG systems with Gemini, the way you chunk your text significantly impacts retrieval quality and latency. Standard fixed-size chunking often splits semantic meaning across boundaries.

lightbulbThe Task

Implement a semantic chunking function that respects paragraph boundaries while ensuring no chunk exceeds the MAX_TOKENS limit.

Instructions

  1. Examine the split_text function in the editor.
  2. Update the logic to split by \n\n instead of a fixed character count.
  3. Ensure that if a single paragraph exceeds the token limit, it falls back to sentence splitting.
check
All tests passed
+250 XP · advancing…
codechunker.py
descriptiontest_chunker.py
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1 2 3 4 5 6 7 8 9 10 11 12 13 14
import tiktoken

def semantic_chunk(text, max_tokens=500):
    """
    Splits text into chunks prioritizing semantic boundaries.
    """
    encoder = tiktoken.get_encoding("cl100k_base")

    # TODO: Implement semantic splitting logic here
    # Currently using naive character splitting
    chunks = []
    for i in range(0, len(text), 1000):
        chunks.append(text[i:i+1000])

    return chunks
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my_locationSee the highlighted lines in chunker.py →
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Module Complete

Data Pipeline Optimization

You cleared all 8 steps. Nicely done — that's production-grade chunking.

+250
XP EARNED
8/8
STEPS
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One step from the 'RAG Architect' badge
Beat the boss battle to unlock it.

Gemini FDE Certify

| Boss Battle: The Skeptical CEO BOSS BATTLE
timer{{ clock }}
CEO

The Skeptical CEO

Level 50 Executive Constraint

Stage 3: The Cost Optimization Demand

NO HINTS ALLOWED

"Your proposed architecture is impressive, but it's 30% over budget. I need you to optimize the token usage without degrading the model's output quality for our critical financial reporting feature."

warningConstraint

Rewrite the system prompt to enforce strict JSON output while utilizing a smaller model tier (Gemini Flash instead of Pro), maintaining the 99% accuracy requirement.

Boss Defeated! +1,500 XP

You held the line on quality and cost. The CEO is impressed.
codeSystem Prompt Editor
1 2 3 4 5 6 7 8
terminalOutput
Ready to execute prompt. Waiting for input...

Gemini FDE Certify

| Context Management VISUAL BUILDER
400 XP
account_treeMemory Module

Branching Context Flows

Long conversations overflow the context window. Production agents stay coherent by summarizing history and retrieving long-term memory in parallel, then merging both before the model call.

lightbulbThe Task

Wire the pipeline so Context Assembler branches into both Summarize and Retrieve Memory, then both feed the model before the response.

Node palette

input
Input / Outputdrag_indicator
summarize
Summarizerdrag_indicator
database
Memory Storedrag_indicator
smart_toy
Gemini Modeldrag_indicator
chat
User Query
input
dashboard_customize
Context Assembler
branch ×2
summarize
Summarize
history → gist
database
Retrieve Memory
vector top-k
smart_toy
Gemini 1.5 Pro
merged context
output
Response
final answer
check
terminalExecution trace
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task_altPipeline ran clean — response in 1.4s
errorFailed at Retrieve Memory · cold cache
verifiedValid pipeline
Gemini FDE Certify AI ROI & Value ROI MODULE
savingsLESSON 1 / 5 · MEASURING VALUE

Does this AI system actually pay for itself?

ROI is the number that decides whether your deployment survives the next budget review. Measure the value created (hours saved × loaded cost), subtract the cost to run it (mostly tokens + infra), and express the rest as a percentage and a payback window.

THE FORMULA
ROI % = (value − cost) ÷ cost × 100
value = seats × hrs saved × 4.33 × rate
TOKEN COST REFERENCE · per 1M tokens
MODEL
INPUT
OUTPUT
Gemini Flash
$0.075
$0.30
Gemini Pro
$1.25
$5.00
Gemini Ultra
$7.00
$21.00
IS YOUR SYSTEM ROI-HEALTHY?
check_circlePayback under 90 days — the system recoups its build + run cost within a quarter.
check_circleToken cost < 30% of value — route cheap calls to Flash, reserve Pro for the hard ones.
check_circleValue is attributable — you can tie hours saved to a baseline you measured before launch.
calculate

ROI Calculator

NET MONTHLY ROI
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PAYBACK PERIOD
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VALUE CREATED{{ roiMonthlyValue }}
COST TO RUN{{ roiCostLabel }}
Net value / month{{ roiNet }}
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Gemini FDE Certify Workflow Lab HUB & SPOKE
account_treeWORKFLOW LAB · LESSON 4/11

Build a hub-and-spoke RAG agent

A hub-and-spoke architecture puts one orchestrator at the center. It decomposes the goal, fans work out to independent tools, then merges results. Tools never call each other — they only talk to the hub.

YOUR TASK
The flow on the right answers a revenue question using web search, RAG retrieval, and a calculator. Press Run and watch each hop. One tool will fail — find out which, why, and how the orchestrator recovers.
WHAT TO LOOK FOR
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account_treerevenue_agent.flow
toolagenterror
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ads_click
Press Run to execute the workflow
Each hop streams its input → output. Click any step for the full message.
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check
Goal achieved ✓
The agent recovered from the calculator error and returned a correct, cited answer.
+120 XP
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