Prompt Engineering

Prompt Engineering for AI Agents

2-4 hours 4 Modules

Overview

Main Topic: Prompt Engineering for Automation

Workload: 2 - 4 Hours

Level: Advanced

Target Audience: Developers, process analysts (BI/Ops), and system integrators.

ABOUT THE TRAINING

A prompt for a human to read is fundamentally different from a prompt for software to process. This training focuses on stability and data formatting, teaching how to create instructions that connect the intelligence of LLMs to automated flows in tools like n8n, Make, or via direct APIs. It is the link between AI creativity and the rigidity required in IT systems.

Objective

Empower the professional to connect LLM models to manual tasks in autonomous and robust processes, ensuring that AI delivers clean and predictable data that does not break daily integrations.

TRAINING MODULES

Structured Outputs (JSON/XML)

Advanced techniques to ensure that AI strictly returns clean code, without "conversations" or additional explanations, ready to be injected into databases or legacy systems.

Exception Handling and Failure Logic

Prompt programming to deal with the absence of information. Instead of allowing hallucinations, the student learns to set up default value returns (such as null or N/A) when critical data is not found in the original text.

APE Technique (Automatic Prompt)

Using the AI itself to automatically generate and optimize complex prompts, increasing the scale and efficiency of the operation.

Resilience and Validation Logic Lab

Practical exercise of "Stress Test" and self-correction. The student will learn to create validation layers within the prompt itself (Self-Correction). The challenge is to simulate noisy or contradictory data inputs to test if the AI can identify the inconsistency and format the output according to a pre-defined schema.

Additional materials

Prompt sent:

You are an Operational Intelligence Agent focused on Crisis Management. Analyze the incoming support log and determine how critical it is. Your answer will feed an automated flow in n8n/Make that fires immediate alerts to the leadership team.

Reasoning instructions:

1. Sentiment analysis: Identify whether the tone is aggressive or signals an intent to take legal action.
2. Category identification: Classify it as: Billing, Technical, Legal or Reputational.
3. Exception handling: If the input is in another language or is unintelligible, return null in the field score_prioridade and flag it for human review.
4. JSON output: Return a JSON object with the data

Generated result:

Full JSON


  {
    "analise_sentimento": {
      "tom": "agressivo",
      "indicadores": [
        "ameaça explícita de ação judicial",
        "uso de linguagem acusatória",
        "exigência de prazo imediato",
        "ameaça de exposição pública"
      ],
      "risco_juridico": true,
      "risco_reputacional": true
    },
    "categoria_principal": "Faturamento",
    "categorias_secundarias": [
      "Jurídico",
      "Reputacional"
    ],
    "score_prioridade": 10,
    "nivel_criticidade": "CRÍTICO",
    "justificativa": "Cliente relata cobrança muito acima da média histórica, atribui erro à empresa, ameaça ação judicial por danos morais e cobrança indevida, além de exposição em redes sociais com alto alcance. Exige correção em prazo de 24h.",
    "acao_recomendada": [
      "Escalonar imediatamente para liderança de CX",
      "Acionar time jurídico preventivamente",
      "Priorizar auditoria de faturamento e leitura de medidor",
      "Responder ao cliente em até 1 hora com posicionamento formal"
    ],
    "origem": "Ouvidoria",
    "necessita_intervencao_humana": false
  }