11. Prompt Chaining

"Write a blog post about microservices."

flowchart TD
    subgraph ONEPROMPT["Single Prompt"]
        O["'Write a blog post\nabout microservices'"] --> O1["❌ Generic,\npoorly structured"]
    end

    subgraph CHAIN["Prompt Chain"]
        C1["1. Research\ntopic"] --> C2["2. Create\noutline"]
        C2 --> C3["3. Write\nsection 1"]
        C3 --> C4["4. Write\nsection 2"]
        C4 --> C5["5. Add\nexamples"]
        C5 --> C6["6. Edit\n&\nPolish"]
    end

    style ONEPROMPT fill:#ef4444,color:#fff
    style CHAIN fill:#22c55e,color:#fff

flowchart TD
    Q1["Can the task be done in\n1-2 LLM calls?"]
    Q1 -->|Yes| SINGLE["Use Single Prompt\nSimpler, cheaper"]
    Q1 -->|No| Q2["Does it need different\ncontext for each step?"]
    Q2 -->|Yes| CHAIN["Use Prompt Chain\nSpecialized per step"]
    Q2 -->|No| Q3["Is intermediate output\nuseful to inspect?"]
    Q3 -->|Yes| CHAIN
    Q3 -->|No| Q4["Is the task complex\n(5+ subtasks)?"]
    Q4 -->|Yes| CHAIN
    Q4 -->|No| SINGLE

    style SINGLE fill:#22c55e,color:#fff
    style CHAIN fill:#3b82f6,color:#fff

flowchart LR
    S1["Prompt 1\nInstruction + Input"] --> O1["Output 1"]
    O1 --> S2["Prompt 2\nPrevious output + new instruction"]
    S2 --> O2["Output 2"]
    O2 --> S3["Prompt 3\nPrevious output + new instruction"]
    S3 --> O3["Final Output"]

    style S1 fill:#3b82f6,color:#fff
    style S2 fill:#f59e0b,color:#fff
    style S3 fill:#22c55e,color:#fff

flowchart TD
    INPUT["Input Document"] --> S1["Prompt 1\nSummarize"]
    INPUT --> S2["Prompt 2\nExtract entities"]
    INPUT --> S3["Prompt 3\nSentiment analysis"]

    S1 --> COMBINE["Combine Results"]
    S2 --> COMBINE
    S3 --> COMBINE
    COMBINE --> OUTPUT["Structured Output"]

    style S1 fill:#3b82f6,color:#fff
    style S2 fill:#f59e0b,color:#fff
    style S3 fill:#22c55e,color:#fff
    style COMBINE fill:#8b5cf6,color:#fff

flowchart TD
    CLASSIFY["Classify\ntype of request"] -->|"Refund"| REFUND["Refund\nPipeline"]
    CLASSIFY -->|"Technical"| TECH["Technical\nSupport Pipeline"]
    CLASSIFY -->|"Feedback"| FEEDBACK["Feedback\nProcessing"]

    style CLASSIFY fill:#f59e0b,color:#fff
    style REFUND fill:#3b82f6,color:#fff
    style TECH fill:#22c55e,color:#fff
    style FEEDBACK fill:#8b5cf6,color:#fff

flowchart LR
    S1["Prompt 1\nResearch topic\n& gather facts"] --> O1["Research Notes"]
    O1 --> S2["Prompt 2\nCreate outline\nwith sections"]
    S2 --> O2["Outline"]
    O2 --> S3["Prompt 3\nWrite section 1\n(Introduction)"]
    O2 --> S4["Prompt 4\nWrite section 2\n(Main content)"]
    O2 --> S5["Prompt 5\nWrite section 3\n(Examples)"]
    O3["Section 1"] --> S6["Prompt 6\nCombine, edit,\nadd conclusion"]
    O4["Section 2"] --> S6
    O5["Section 3"] --> S6
    S6 --> FINAL["Final Post"]

    style S1 fill:#3b82f6,color:#fff
    style S2 fill:#f59e0b,color:#fff
    style S6 fill:#22c55e,color:#fff

Step 1 — Document Classification
Prompt: "Classify this document type: invoice, receipt, contract, or other.
Document: [text]"
Output: {"document_type": "invoice", "confidence": 0.95}

Step 2 — Schema Selection (conditional)
If invoice → use invoice extraction schema
If contract → use contract extraction schema

Step 3 — Data Extraction
Prompt: "Extract the following fields from this invoice:
{{schema}}. Invoice text: {{text}}"
Output: {"vendor": "...", "amount": "..."}

Step 4 — Validation
Prompt: "Validate this extracted data. Are any fields missing or
unreasonable? Data: {{extracted_data}}"
Output: {"valid": true, "warnings": []}

Step 5 — Formatting
Prompt: "Format this data for the accounting system.
Schema: {{target_schema}}. Data: {{validated_data}}"
Output: Final formatted JSON

✅ Good: Each prompt does one thing well
  - Prompt 1: Extract entities
  - Prompt 2: Classify sentiment
  - Prompt 3: Generate summary

❌ Bad: Prompt does too much
  - "Extract entities, classify sentiment, and generate a summary all at once"

✅ Pass JSON between steps
  Step 1 Output: {"entities": [...], "summary": "..."}
  Step 2 Input: "You received this data: {{json}}. Now categorize..."

❌ Pass free text
  Step 1 Output: "I found some entities like..."
  Step 2 Input must parse free text

async function runChain(input) {
  const step1 = await callLLM(step1Prompt(input));
  const validated1 = validateStep1(step1);
  if (!validated1.valid) throw new Error(`Step 1 failed: ${validated1.error}`);

  const step2 = await callLLM(step2Prompt(validated1.data));
  const validated2 = validateStep2(step2);
  if (!validated2.valid) throw new Error(`Step 2 failed: ${validated2.error}`);

  return validated2.data;
}

Step 1 — Intent Classification
Input: Customer message
Output: {intent: "refund", urgency: "high", sentiment: "frustrated"}

Step 2 — Policy Lookup (conditional)
If refund → Look up refund policy for this product
Output: Policy rules

Step 3 — Response Generation
Input: Intent + Policy + Customer Message
Output: Draft response

Step 4 — Tone Adjustment
Input: Draft + Sentiment
Output: Empathetic, professional response

Step 5 — Quality Check
Input: Response + Intent
Output: {passes_quality: true, issues: []}

Step 1: Analyze source code
Step 2: Map patterns (JS → TypeScript, Express → Fastify)
Step 3: Generate converted code
Step 4: Review for issues
Step 5: Generate tests
Step 6: Document changes

from langchain.chains import LLMChain, SequentialChain

chain1 = LLMChain(llm=llm, prompt=research_prompt)
chain2 = LLMChain(llm=llm, prompt=outline_prompt)
chain3 = LLMChain(llm=llm, prompt=write_prompt)

overall_chain = SequentialChain(
    chains=[chain1, chain2, chain3],
    input_variables=["topic"],
    output_variables=["final_article"]
)

import { generateText, generateObject } from 'ai';

const { object: intent } = await generateObject({ model, prompt, schema: intentSchema });
const { text: response } = await generateText({ model, prompt: responsePrompt(intent) });