Chain LLM Pattern for n8n

Multi-step LLM chains outperform single-shot prompts on any task that combines extraction + reasoning. This skill encodes the production pattern.

When to use a chain vs a single prompt

Rule of thumb: if the task has ≥2 distinct reasoning steps OR the final decision depends on intermediate structured data, use a chain.

The pattern (3-stage default)

Input → [Extract] → [Analyze/Classify] → [Score/Decide] → Output

Each stage is its own LLM node with its own prompt. Between stages, use Set or Code nodes to transform and validate.

Stage 1: Extract (schema-bound)

Use Information Extractor node (LangChain). NOT a generic AI Agent or raw HTTP call.

Why: Information Extractor binds output to a JSON schema. It parses, retries on invalid JSON, and fails loudly — instead of silently returning prose you then regex.

Define schema explicitly:

{
  "type": "object",
  "properties": {
    "customer_name": { "type": "string" },
    "product_mentioned": { "type": "string" },
    "sentiment": { "enum": ["positive", "neutral", "negative"] },
    "urgency_score": { "type": "number", "minimum": 0, "maximum": 10 }
  },
  "required": ["customer_name", "sentiment"]
}

System prompt for this stage: short, one job. "Extract the fields defined in the schema from the transcript. If a field is absent, omit it. Do not infer or guess."

Stage 2: Analyze (reason over extracted data)

Use Basic LLM Chain with the extracted JSON from Stage 1 as input.

This stage reasons: categorize, cluster, identify patterns, detect issues. The input is structured (from Stage 1) so the model isn't juggling parsing + reasoning simultaneously.

Example system prompt:

Given the extracted customer data below, classify into one of: [technical_issue, billing_question, cancellation_risk, upsell_opportunity]. Then identify the single most important next action. Return JSON with category and next_action.

Stage 3: Score / decide (deterministic where possible)

If the final step is arithmetic (e.g., composite scoring: 0.4 × email_score + 0.3 × phone_score + 0.3 × content_score), use a Code node, NOT an LLM.

LLMs are bad at arithmetic. They fail silently. Use Code (JavaScript) for any math involving weights, thresholds, or aggregation.

Model selection

Groq is the fastest provider for extract stages — 500+ tokens/sec. Use it unless you need Claude/OpenAI specifically.

Production rules

1. Pin example data at each stage during development. Right-click node → "Pin Data". Without pinning, changing Stage 1 invalidates all downstream test data and you waste API calls.
2. Budget tokens explicitly. Set maxTokens on every LLM node. Stage 1 extract rarely needs >500. Stage 2 analyze rarely >1000.
3. Validate between stages. Insert a Code node between LLM stages that checks required fields exist. Fail fast with a clear error — don't let a missing field propagate and produce a confusing Stage 3 failure.
4. Log stage outputs. Add a MySQL or Google Sheets insert after Stage 1 and Stage 2 that records the raw output (truncated to 1000 chars). You WILL need this for debugging.
5. Temperature: 0 for extract, 0.2–0.4 for analyze, 0 for scoring. Extract must be deterministic. Analysis benefits from slight variance. Scoring must be deterministic.

Common multi-language variant (EN/ES/PT)

For transcripts in mixed languages, add a Stage 0:

Stage 0 (Groq): Detect language → route to language-specific prompts
Stage 1 (language-specific): Extract in source language
Stage 2: Translate structured output to English (cheap, short)
Stage 3: Analyze in English

Language-specific prompts extract better than a single multilingual prompt because entity names (cities, products) follow different patterns per language.

Anti-patterns

• Don't use AI Agent for extraction. Agents are for tool use, not structured output. Use Information Extractor.
• Don't concatenate all steps into one mega-prompt. Each hallucination compounds. Separate stages let you evaluate each independently.
• Don't loop an LLM on retry without a counter. Infinite loops cost money. Cap retries at 3 via a counter in a Set node.

Reference

• references/groq-chain-example.json — a working 4-node chain ready to import into n8n