Test your prompt now with one of these AI assistants to see real-world results:

Keep track of your prompts and the results obtained so you can compare and improve them.

Tip:

Test with an LLM

# Project Report: HR Digitalization

## 1. Context
Our company has 500 employees across 3 sites. Currently, HR management is entirely manual with scattered Excel files.

## 2. Identified Problems
- File processing time: 15 days on average
- Data entry error rate: 12%
- Difficulty tracking applications
- No visibility on performance

## 3. Proposed Solution
Implement an ATS (Applicant Tracking System) coupled with a performance management tool.

## 4. Budget and Timeline
Estimated budget: €50,000
Implementation period: 6 months
Expected ROI: 30% productivity gain

Document

Create a prompt chain to analyze this report (3 pages)

🔗 CHAIN ARCHITECT

**Report Summary:**
- Context: Company of 500 employees, currently manual HR management
- Problems: Slow processing (15d), errors (12%), no tracking
- Solution: Implementation of an ATS with performance management
- Budget: €50,000 over 6 months
- Expected ROI: 30% productivity

STEP

⚠️ Step {index}: Insufficient context. Reference the results from the previous step.

Suggestions:

TASK DECOMPOSITION SUCCESSFUL. VERIFIABLE OUTPUTS GENERATED.

SEQUENCE_COMPLETE

CHAIN_ARCHITECT_V1.0

INITIATING CHAINING SEQUENCE SIMULATION. OBJECTIVE: DECOMPOSE COMPLEX TASK INTO VERIFIABLE STEPS.

SUMMARIZE_DATA

GENERATE_ACTIONS

>> SEQUENCE_PROTOCOL_INIT

Constraints

Toggle switches to see how constraints radically transform the model's response for the same topic (The Eiffel Tower).

Constraint Laboratory

The meeting with Mr. Dupont will take place on March 12 in Lyon to discuss Project Alpha.

See how adding examples (shots) drastically improves response accuracy and formatting, without changing the main instruction.

Extract entities (Name, Date, Location) from the following text.

The Few-Shot Builder

Observe how the model "guesses" the next word. Adjust the temperature to see the impact on its choices.

Prediction Simulator (Token by Token)

Correct this email to be professional: [EMAIL]

Email Correction

Ex: Summarize the following text in 3 key points: [TEXT]

Title

Ex: Article Summarizer

Start building your library of reusable prompts.

Your Prompt-Book

Visualize your progress and skills acquired in this module.

Skill Tree

BUG HUNTER EXPERT

EXERCISE COMPLETED

Missing Colon

Missing Quotes

Parasitic Text

Trailing Comma

FIX THE BUGS

FIND THE VALID JSON

DISCOVER JSON ERRORS

Master the 4 most common JSON errors

BUG HUNTER

Extractor forged!

Delimiters

You are an expert HR assistant in CV analysis.

Role

Expected format:
{
  "name": "(text)",
  "email": "(text)",
  "phone": "(text or null)",
  "skills": ["(list)"],
  "experiences": [{ "position": "", "company": "", "dates": "" }]
}

JSON Schema

Extract the information and return ONLY valid JSON. NO text before or after.

Strict instruction

Include ALL fields even if empty (null or []). Verify JSON syntax before responding.

Verification

Forge your CV extractor piece by piece

JSON extracted successfully!

Real-world test

CV FORGE

JSON

Structured List

Structured Report

Table

Find the ideal format for your use case

FORMAT SELECTOR

Personalized ROI Calculator

Finance

Human Resources

Customer Service

Visualize the transformation of your productivity

IMPACT TIMELINE

Alex's Situation

Perfect!

Transform data into JSON

Exercise Completed!

Validation quiz

Transform chaos into structure

OUTPUT TRANSFORM LAB

Adjust each channel to build the perfect prompt

PROMPT MIXER

Self-Consistency: Democratic vote of multiple reasoning paths

CONSENSUS_ENGINE

CoT vs ReAct: The key difference

I need to first know the current weather in Paris. I will use the weather tool.

{ 'city': 'Paris', 'temp': 14, 'condition': 'Light rain', 'precipitation': 80% }

It's raining lightly with 80% precipitation. I should recommend an umbrella.

format_response(weather_data, recommendation)

In Paris, it's 14°C with light rain. Yes, take an umbrella! ☔

Unlike CoT which only "thinks", ReAct allows the model to <strong>act</strong>:

Module 4: Agents & Orchestration

Reasoning + Acting: The AI that can act

ReAct_PREVIEW

Step back before solving: satellite view → detail view

STEP_BACK_SIMULATOR

OpenAI officially recommends step-by-step decomposition. These techniques are now standard in modern AI agents (ChatGPT, Claude, Gemini).

Technique Comparator

CoT Formalization: showing reasoning improves LLMs on complex problems.

Wei et al. - Chain-of-Thought Prompting

Discovery: 'Let's think step by step' activates CoT without examples.

Kojima et al. - Zero-Shot Reasoners

Majority voting over multiple reasoning paths improves +10-15%.

Wang et al. - Self-Consistency

Taking a step back (identifying principles) before solving avoids errors.

Zheng et al. - Step-Back Prompting

Chain-of-Thought

Self-Consistency

Step-Back

Research Timeline

Identify the formula: Distance = Speed × Time

Brain Scanner: Impulsive Mode vs Reflective Mode (CoT)

THINKING_LAB

Evaluation

Principle

Pruning

Tree-of-Thought: Explore multiple reasoning branches in parallel

THOUGHT_TREE_EXPLORER

Left bank: 🐺 Wolf, 🐐 Goat, 🥬 Cabbage

A company receives 100 CVs per day. Without automation, an HR spends 5 minutes per CV. With an LLM pipeline, it drops to 10 seconds.

The second prompt takes the JSON and analyzes the fit for the position (Senior Dev). It checks key skills.

Step 2: Analysis

The last prompt decides whether to interview the candidate and drafts the response email (invitation or rejection).

Step 3: Decision

The first prompt extracts raw data from the CV and transforms it into structured JSON format. This is easier to process for the next steps.

Step 1: Extraction

A pipeline is a chain of prompts where **the output of one step becomes the input of the next**. It's like a production line: each station does ONE specialized task.

What is a Pipeline?

HR Application Processing

🚦 Smart Routing: Route requests to the right track

ROUTING_SWITCH

Calculation

Advice

Factual Question

Complaint

🔄 Parallel Processing + Global Synthesis

MAP_REDUCE_ENGINE

Alex needs to analyze 200 customer reviews to produce an executive report. A single prompt cannot reliably process that much data.

BUSINESS CONTEXT

The Map-Reduce Pattern

Each review was processed independently with the same prompt. We can parallelize these calls to go 5x faster! Now we need to synthesize these 5 analyses.

INSIGHT:

MAP PHASE: Individual Analysis

Consolidate the {count} analyses into a report

REDUCE PHASE: Global Synthesis

Quality product, fast delivery. Only downside: packaging was slightly damaged.

Disappointed by customer service. Couldn't reach anyone for 3 days about an order issue.

Excellent! Exactly what I was looking for. Highly recommend. Very competitive price.

Okay but nothing special. Product matches description but quality could be better.

Very disappointed. Product arrived broken and no response from support. Do not recommend at all.

200 reviews × 0.1s/review + 5s synthesis = 25 seconds

200 reviews × 3 min/review = 10h manual work

CALCULATED ROI

Synthesis

Map-Reduce Pattern Summary

Always start with the simplest pattern that meets your needs. Only add complexity if truly necessary!

GOLDEN RULE:

Overview

Pattern Comparison

🔍 Find the pattern suited to your use case

COMPLEXITY_ADVISOR

The Decision Tree

Alex is unsure which architecture to choose for their project. Simple prompt? Pipeline? Map-Reduce? Agents? How to decide?

THE PROBLEM

Parallel processing of N items with the same prompt.

Batch Processing

MAP each item, then REDUCE into global synthesis.

Map-Reduce

Buffer for a few seconds then batch processing.

Micro-Batch

Dependency graph with conditional execution.

DAG Orchestration

Chain of steps where each prompt enriches the next's context.

Sequential Pipeline

Queue Workers

Initial classification then redirect to appropriate processing.

Intelligent Routing

A single API call with a well-structured prompt is enough.

Direct Prompt

Token-by-token processing with progressive response.

Streaming Events

✓ ADVANTAGES

✗ DRAWBACKS

JOURNEY_MAP

Map-Reduce Pattern

Pattern Selection

Module 5 (Agents) is the logical next step. You now master the patterns that enable building sophisticated agents!

TIP:

Advanced modules awaiting you

The Journey Ahead

Bots that plan and execute complex tasks autonomously.

Module 5: Autonomous Agents

Connect your LLMs to your own data sources and documents.

Module 6: RAG & Knowledge

Deploy robust LLM solutions in production.

Module 7: Production Ready

You've completed Module 4 and now master the fundamental patterns of composed LLM systems.

Module 5 — Anchor AI in Reliable Sources (RAG)

Target_Objectives.jsonREAD_ONLY

Directory_Structure

Capabilities_Matrix