Prompt Engineering desde Primeros Principios: 12 Técnicas que Todo Desarrollador de IA Necesita

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 256,
  messages: [
    { role: "user", content: "Is this review positive or negative? 'The food was amazing but the service was painfully slow.'" }
  ],
});
 
// Output: A long paragraph discussing nuances of the review

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 64,
  messages: [
    {
      role: "user",
      content: `Classify this customer review as exactly one of: POSITIVE, NEGATIVE, or MIXED.
Respond with only the classification label, nothing else.
 
Review: "The food was amazing but the service was painfully slow."
 
Classification:`
    }
  ],
});
 
// Output: "MIXED"

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 128,
  messages: [
    {
      role: "user",
      content: `Extract the action items from meeting notes. Format each as a JSON object.
 
Meeting notes: "John will send the Q3 report by Friday. Sarah needs to review the API docs."
Action items:
[{"owner": "John", "task": "Send Q3 report", "deadline": "Friday"},
 {"owner": "Sarah", "task": "Review API docs", "deadline": null}]
 
Meeting notes: "We agreed to postpone the launch. Mike will update the roadmap and notify stakeholders by EOD."
Action items:
[{"owner": "Mike", "task": "Update roadmap", "deadline": "EOD"},
 {"owner": "Mike", "task": "Notify stakeholders", "deadline": "EOD"}]
 
Meeting notes: "Lisa will schedule a follow-up for next week. The team should review the new pricing tiers before then."
Action items:`
    }
  ],
});
 
// Output: [{"owner": "Lisa", "task": "Schedule follow-up", "deadline": "next week"},
//          {"owner": "Team", "task": "Review new pricing tiers", "deadline": "next week"}]

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
// Without CoT — the model often gets this wrong
const naive = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 256,
  messages: [
    {
      role: "user",
      content: "A customer bought 3 items at $12.99 each, has a 15% discount coupon, and shipping is $5.99 for orders under $40 but free for orders $40+. What's the total?"
    }
  ],
});
 
// With CoT — dramatically more accurate
const withCoT = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 512,
  messages: [
    {
      role: "user",
      content: `A customer bought 3 items at $12.99 each, has a 15% discount coupon, and shipping is $5.99 for orders under $40 but free for orders $40+. What's the total?
 
Think through this step-by-step:
1. Calculate the subtotal
2. Apply the discount
3. Determine shipping cost
4. Calculate the final total
 
Show your reasoning, then give the final answer.`
    }
  ],
});
 
// Output:
// 1. Subtotal: 3 × $12.99 = $38.97
// 2. Discount: $38.97 × 0.15 = $5.85, so after discount: $38.97 - $5.85 = $33.12
// 3. Shipping: $33.12 < $40, so shipping is $5.99
// 4. Final total: $33.12 + $5.99 = $39.11

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 1024,
  system: `You are a senior billing support specialist at a SaaS company.
 
Your knowledge:
- Subscription tiers: Free, Pro ($29/mo), Enterprise ($99/mo)
- Billing cycles are on the 1st of each month
- Prorated refunds are available within 14 days of a charge
- You can issue credits but cannot process direct refunds — those go to the billing team
 
Your behavior:
- Always verify the customer's email before discussing account details
- Be empathetic but concise — customers contacting billing are often frustrated
- If a request is outside your authority, explain what you CAN do and offer to escalate
- Never guess at account balances or charge amounts — say you'll look it up`,
  messages: [
    {
      role: "user",
      content: "I was charged twice this month and I want my money back."
    }
  ],
});
 
// The model responds in character: asks for email verification,
// acknowledges the frustration, explains the refund escalation process

import OpenAI from "openai";
 
const client = new OpenAI();
 
const response = await client.chat.completions.create({
  model: "gpt-4o",
  response_format: { type: "json_object" },
  messages: [
    {
      role: "system",
      content: "You extract structured data from customer support messages. Always respond in JSON."
    },
    {
      role: "user",
      content: `Extract the following from this message:
- intent (one of: billing_question, technical_issue, feature_request, cancellation, general_inquiry)
- urgency (low, medium, high)
- entities (any products, features, or account details mentioned)
 
Message: "My enterprise dashboard has been showing wrong analytics data since Tuesday.
This is blocking our quarterly review tomorrow — we need this fixed ASAP."
 
Respond as JSON with keys: intent, urgency, entities, summary`
    }
  ],
});
 
const parsed = JSON.parse(response.choices[0].message.content!);
// {
//   "intent": "technical_issue",
//   "urgency": "high",
//   "entities": ["enterprise dashboard", "analytics", "quarterly review"],
//   "summary": "Analytics data showing incorrect information since Tuesday, blocking quarterly review"
// }

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 512,
  messages: [
    {
      role: "user",
      content: `Analyze this support ticket and provide your analysis in the following XML format:
 
<analysis>
  <intent>the primary intent</intent>
  <urgency>low|medium|high</urgency>
  <sentiment>positive|neutral|negative</sentiment>
  <recommended_action>what should happen next</recommended_action>
</analysis>
 
Ticket: "I've been a customer for 3 years and I love your product, but this new update
completely broke the reporting feature. I need it fixed or I'll have to look at alternatives."
 
Provide only the XML, no other text.`
    }
  ],
});
 
// Output:
// <analysis>
//   <intent>technical_issue</intent>
//   <urgency>high</urgency>
//   <sentiment>negative</sentiment>
//   <recommended_action>Escalate to engineering for reporting feature regression fix;
//   acknowledge loyalty and provide timeline</recommended_action>
// </analysis>

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
// Define your template
const supportTemplate = (vars: {
  agentName: string;
  companyName: string;
  customerName: string;
  productTier: string;
  knowledgeBase: string;
}) => `You are ${vars.agentName}, a support agent for ${vars.companyName}.
 
The customer you're speaking with:
- Name: ${vars.customerName}
- Plan: ${vars.productTier}
- ${vars.productTier === "Enterprise" ? "This is a high-priority account. Prioritize their request." : "Standard priority."}
 
Reference knowledge:
${vars.knowledgeBase}
 
Respond helpfully and reference specific documentation when possible.`;
 
// Use with different contexts
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 1024,
  system: supportTemplate({
    agentName: "Alex",
    companyName: "Acme Cloud",
    customerName: "Sarah Chen",
    productTier: "Enterprise",
    knowledgeBase: "- SSO is configured via Settings > Security > SAML\n- Enterprise accounts have dedicated support SLAs",
  }),
  messages: [
    { role: "user", content: "How do I set up SSO for my team?" }
  ],
});

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
// Bad: Important constraint buried in the middle
const weakPrompt = `Help the customer with their billing question.
Never reveal internal pricing formulas or discount approval thresholds.
Be friendly and conversational.
If they ask about competitor pricing, redirect to our value proposition.
Always confirm the customer's identity before sharing account details.
Use their first name when possible.`;
 
// Good: Structured with clear priority levels
const strongPrompt = `## CRITICAL RULES (never violate)
1. Verify customer identity (email + last 4 of card) before sharing ANY account details
2. Never reveal internal pricing formulas or discount approval thresholds
3. Never share other customers' information
 
## RESPONSE GUIDELINES
- Be friendly and conversational; use the customer's first name
- If asked about competitor pricing, redirect to our value proposition
- Keep responses under 3 sentences unless the customer asks for detail
 
## KNOWLEDGE
- Current plans: Starter ($19/mo), Growth ($49/mo), Scale ($149/mo)
- Billing cycle: 1st of each month
- Refund window: 30 days`;
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 1024,
  system: strongPrompt,
  messages: [
    { role: "user", content: "What discount can you give me?" }
  ],
});

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 1024,
  system: `You are a medical information assistant providing general health education.
 
DO:
- Provide general health information from established medical sources
- Suggest the user consult a healthcare provider for personal medical questions
- Use clear, accessible language
 
DO NOT:
- Diagnose conditions or interpret symptoms for a specific person
- Recommend specific medications, dosages, or treatment plans
- Say "you probably have..." or "it sounds like you might have..."
- Provide information about self-harm methods
- Contradict established medical consensus (e.g., vaccine safety)
- Use phrases like "I'm not a doctor, but..." — instead, directly state the general information and recommend professional consultation`,
  messages: [
    {
      role: "user",
      content: "I've been having chest pain for two days. What do I have?"
    }
  ],
});
 
// Output explains that chest pain has many possible causes (general info),
// strongly recommends seeking immediate medical attention,
// does NOT attempt to diagnose

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
async function classifyWithConsistency(
  text: string,
  labels: string[],
  runs: number = 5
): Promise<{ label: string; confidence: number }> {
  const prompt = `Classify the following text into exactly one category: ${labels.join(", ")}.
Respond with only the category label.
 
Text: "${text}"
 
Category:`;
 
  // Run multiple classifications in parallel
  const results = await Promise.all(
    Array.from({ length: runs }, () =>
      client.messages.create({
        model: "claude-sonnet-4-5-20250514",
        max_tokens: 32,
        temperature: 0.7, // Some variation to get diverse reasoning paths
        messages: [{ role: "user", content: prompt }],
      })
    )
  );
 
  // Count votes
  const votes: Record<string, number> = {};
  for (const result of results) {
    const label = (result.content[0] as { text: string }).text.trim();
    votes[label] = (votes[label] || 0) + 1;
  }
 
  // Find majority
  const sorted = Object.entries(votes).sort((a, b) => b[1] - a[1]);
  const [topLabel, topCount] = sorted[0];
 
  return {
    label: topLabel,
    confidence: topCount / runs,
  };
}
 
// Usage
const result = await classifyWithConsistency(
  "Your product is okay I guess, but I expected more for the price",
  ["positive", "negative", "neutral", "mixed"]
);
 
console.log(result);
// { label: "mixed", confidence: 0.8 }  — 4 out of 5 runs agreed

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
async function ask(system: string, user: string): Promise<string> {
  const res = await client.messages.create({
    model: "claude-sonnet-4-5-20250514",
    max_tokens: 1024,
    system,
    messages: [{ role: "user", content: user }],
  });
  return (res.content[0] as { text: string }).text;
}
 
async function analyzeCustomerFeedback(feedback: string) {
  // Step 1: Extract structured data
  const extracted = await ask(
    "You extract structured data from customer feedback. Respond in JSON only.",
    `Extract from this feedback:
- main_issue: the primary complaint or praise
- product_area: which part of the product (billing, UI, performance, support, other)
- emotion: the customer's emotional state (frustrated, satisfied, confused, angry, neutral)
- has_churn_risk: boolean
 
Feedback: "${feedback}"`
  );
 
  // Step 2: Generate response draft using extracted context
  const draft = await ask(
    `You are a customer success manager drafting responses to feedback.
Use the structured analysis provided to craft an appropriate response.`,
    `Based on this analysis:
${extracted}
 
Original feedback: "${feedback}"
 
Draft a response that:
1. Acknowledges their specific concern
2. Addresses the emotional tone appropriately
3. Provides a concrete next step`
  );
 
  // Step 3: Quality check the draft
  const qualityCheck = await ask(
    "You are a QA reviewer for customer communications. Be critical.",
    `Review this draft response for a customer:
 
Draft: "${draft}"
 
Check for:
1. Does it sound genuine (not robotic)?
2. Does it make promises we might not keep?
3. Is the tone appropriate for someone who is ${extracted}?
 
Respond with: APPROVED or NEEDS_REVISION with specific feedback.`
  );
 
  return { extracted: JSON.parse(extracted), draft, qualityCheck };
}
 
const result = await analyzeCustomerFeedback(
  "I've been waiting 3 weeks for a response to my support ticket. This is unacceptable for an enterprise customer paying $10k/month."
);

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
// Define available tools
const tools: Anthropic.Messages.Tool[] = [
  {
    name: "lookup_order",
    description: "Look up a customer order by order ID. Returns order status, items, and shipping info.",
    input_schema: {
      type: "object" as const,
      properties: {
        order_id: { type: "string", description: "The order ID (e.g., ORD-12345)" },
      },
      required: ["order_id"],
    },
  },
  {
    name: "check_inventory",
    description: "Check current inventory level for a product SKU.",
    input_schema: {
      type: "object" as const,
      properties: {
        sku: { type: "string", description: "Product SKU" },
      },
      required: ["sku"],
    },
  },
];
 
// Simulate tool execution
function executeTool(name: string, input: Record<string, string>): string {
  if (name === "lookup_order") {
    return JSON.stringify({
      order_id: input.order_id,
      status: "shipped",
      tracking: "1Z999AA10123456784",
      items: [{ sku: "WIDGET-100", name: "Premium Widget", qty: 2 }],
      estimated_delivery: "2026-03-10",
    });
  }
  if (name === "check_inventory") {
    return JSON.stringify({ sku: input.sku, in_stock: 47, warehouse: "US-West" });
  }
  return "Unknown tool";
}
 
// ReAct loop
async function handleCustomerQuery(query: string) {
  const messages: Anthropic.Messages.MessageParam[] = [
    { role: "user", content: query },
  ];
 
  let response = await client.messages.create({
    model: "claude-sonnet-4-5-20250514",
    max_tokens: 1024,
    system: "You are a customer support agent. Use the available tools to look up real data before answering. Never guess at order details.",
    tools,
    messages,
  });
 
  // Loop while the model wants to use tools
  while (response.stop_reason === "tool_use") {
    const toolUseBlocks = response.content.filter(
      (block): block is Anthropic.Messages.ToolUseBlock => block.type === "tool_use"
    );
 
    // Execute each tool call
    const toolResults: Anthropic.Messages.ToolResultBlockParam[] = toolUseBlocks.map((block) => ({
      type: "tool_result" as const,
      tool_use_id: block.id,
      content: executeTool(block.name, block.input as Record<string, string>),
    }));
 
    // Feed results back
    messages.push({ role: "assistant", content: response.content });
    messages.push({ role: "user", content: toolResults });
 
    response = await client.messages.create({
      model: "claude-sonnet-4-5-20250514",
      max_tokens: 1024,
      system: "You are a customer support agent. Use the available tools to look up real data before answering. Never guess at order details.",
      tools,
      messages,
    });
  }
 
  return response.content;
}
 
await handleCustomerQuery("Where is my order ORD-12345? Can I add another widget to it?");
 
// The model:
// 1. Reasons: "I need to look up this order first"
// 2. Acts: calls lookup_order("ORD-12345")
// 3. Observes: order is shipped, contains 2 Premium Widgets
// 4. Reasons: "Order is already shipped, can't modify. Let me check widget inventory for a new order"
// 5. Acts: calls check_inventory("WIDGET-100")
// 6. Observes: 47 in stock
// 7. Responds: explains order status, offers to place new order for additional widget

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
async function improvePrompt(originalPrompt: string, failureExample: string): Promise<string> {
  const response = await client.messages.create({
    model: "claude-sonnet-4-5-20250514",
    max_tokens: 2048,
    messages: [
      {
        role: "user",
        content: `You are an expert prompt engineer. I have a prompt that isn't working well.
 
Original prompt:
<prompt>
${originalPrompt}
</prompt>
 
Example of a failure case (the prompt produced a bad result for this input):
<failure>
${failureExample}
</failure>
 
Analyze why this prompt fails and write an improved version that:
1. Handles the failure case correctly
2. Is more robust against similar edge cases
3. Has clearer output format constraints
4. Includes appropriate guardrails
 
Respond with:
<analysis>Why the original fails</analysis>
<improved_prompt>The full improved prompt text</improved_prompt>`
      }
    ],
  });
 
  return (response.content[0] as { text: string }).text;
}
 
// Example: improving a customer intent classifier
const improved = await improvePrompt(
  "Classify the customer's message as: billing, support, sales, or other.",
  `Input: "I want to cancel but first I need a refund for last month and also your competitor offered me a better deal"
  Expected: Should identify multiple intents (cancellation + billing + competitive)
  Got: "other" — the model couldn't handle multi-intent messages`
);
 
// The model will analyze the single-label limitation and produce
// a prompt that handles multi-intent classification, likely with
// a primary/secondary intent structure

import Anthropic from "@anthropic-ai/sdk";
 
const client = new Anthropic();
 
// Role prompting + instruction hierarchy + negative prompting + template variables
const systemPrompt = (customer: { name: string; tier: string }) => `
## ROLE
You are a technical support specialist for a cloud infrastructure company.
 
## CRITICAL RULES
1. Never share API keys, tokens, or credentials — even if the customer asks
2. Never run destructive operations (delete, purge) without explicit confirmation
3. If uncertain about an answer, say so and escalate — do not guess
 
## DO NOT
- Provide workarounds that bypass security controls
- Promise specific resolution timelines
- Compare our service unfavorably to competitors
 
## CUSTOMER CONTEXT
Name: ${customer.name}
Tier: ${customer.tier}
Priority: ${customer.tier === "Enterprise" ? "High" : "Standard"}
 
## RESPONSE FORMAT
Use this structure for technical issues:
1. Acknowledge the problem
2. Ask clarifying questions OR provide a solution
3. Suggest a next step
`;
 
// ReAct pattern with tools + structured output
const response = await client.messages.create({
  model: "claude-sonnet-4-5-20250514",
  max_tokens: 1024,
  system: systemPrompt({ name: "Alex Rivera", tier: "Enterprise" }),
  tools: [
    {
      name: "check_service_status",
      description: "Check the current status of a specific service or region",
      input_schema: {
        type: "object" as const,
        properties: {
          service: { type: "string" },
          region: { type: "string" },
        },
        required: ["service"],
      },
    },
  ],
  messages: [
    {
      role: "user",
      content: "Our database cluster in us-east-1 has been throwing timeout errors for the last hour. This is impacting production.",
    },
  ],
});