Skill LLMs בכלי No-Code ל-Claude

מתי להשתמש

"AI ב-Zapier", "GPT ב-Make", "Claude ב-n8n", "AI workflow", "LLM in automation".

הוראות עבודה

1. כשAI שווה לשלב

✅ מצוין ל

• Classification (sentiment, category, urgency).
• Extraction (extract structured data from text).
• Generation (emails, summaries, descriptions).
• Translation.
• Summarization (long text → short).
• Reformatting (data cleanup).

❌ לא מתאים ל

• Deterministic logic (if-then) — use code.
• Math — LLMs grumpy with numbers.
• Real-time decisions — latency 1-5 sec.
• Cost-sensitive high-volume — operations cost.
• Critical decisions — needs human review.

2. AI Modules — Quick Reference

Zapier

• Zapier AI Actions (built-in OpenAI/Anthropic).
• OpenAI module native.
• Claude/Anthropic via HTTP.

Make.com

• OpenAI module (deep).
• Anthropic Claude module.
• Hugging Face module.
• Custom HTTP for any provider.

n8n

• OpenAI nodes.
• Anthropic Claude nodes.
• LangChain nodes (RAG patterns).
• Hugging Face nodes.

3. Common Use Cases

A. Email Classification

Trigger: New email
Action: Claude classify:
  - Urgency: low/med/high
  - Topic: sales/support/general
  - Sentiment: positive/neutral/negative
Decision: Route based on classification

B. Lead Enrichment from Website Text

Trigger: New lead
Action: Visit company website (HTTP)
Action: Claude extract:
  - Industry
  - Company size estimate
  - Tech stack mentioned
  - Recent news
Update: CRM with extracted data

C. Auto-Reply Drafts

Trigger: New support ticket
Action: Claude draft reply (with context from KB)
Action: Send to agent for review
Agent: Edit + Send

D. Content Generation

Trigger: New product added
Action: Claude generate:
  - Product description (3 versions)
  - Meta description (SEO)
  - Social media post
Action: Save to CMS draft

E. Document Summarization

Trigger: New PDF in Google Drive
Action: Extract text (OCR)
Action: Claude summarize
Action: Email summary + Action items

4. Prompt Design — Best Practices

Structure

You are an expert at [task].

Task: [what you want]

Input: {{variable from previous step}}

Output format:
{
  "field1": "value",
  "field2": "value"
}

Examples:
[1-3 examples]

Constraints:
- Always respond in JSON
- Don't include explanations
- If unsure, set field to "unknown"

Tips

• Be specific about output format.
• Examples are powerful (few-shot prompting).
• JSON output for reliable parsing.
• Constraints prevent hallucinations.

5. Cost Optimization

Token Counting

• 1 token ≈ 4 characters in English.
• Hebrew uses more tokens.
• Output tokens cost 2-5x input tokens.

Pricing 2026 (approximate)

Strategies

• Use cheaper models for simple tasks (Haiku, GPT-4 mini).
• Use expensive for complex (Opus, GPT-4 Turbo).
• Cache common responses.
• Batch when possible.
• Limit output length (max_tokens).
• Skip AI when rule-based works.

6. Error Handling for LLMs

Common Errors

• Rate limit (429) — retry with backoff.
• Timeout (>60 sec) — increase or split.
• JSON parse error — invalid output → retry with stricter prompt.
• Hallucination — wrong/made-up data → validate.

Validation

• After AI response → schema check.
• Required fields present?
• Values in expected range?
• If invalid → retry or fall back.

7. Sample Make.com Workflow with Claude

Module 1: Trigger (New Email in Gmail)

Module 2: Anthropic Claude
  Model: claude-haiku-4-5
  System: "Classify emails into urgent/normal/spam"
  User: {{1.subject}} - {{1.body}}
  Max tokens: 50

Module 3: Router
  ├── Path "urgent" → Slack alert
  ├── Path "normal" → Standard queue
  └── Path "spam" → Move to spam folder

Module 4: Error handler (Resume with default "normal")

8. Privacy Concerns

• PII in prompts = sent to OpenAI/Anthropic.
• Anthropic does NOT train on API data (default).
• OpenAI does NOT train on API data (default).
• Self-hosted (LLaMA/Mistral) for sensitive data.

9. Best Practices

• Start with single use case.
• Test extensively with diverse inputs.
• Monitor cost daily in early days.
• Validate outputs before downstream actions.
• Have fallback when AI fails.

10. Israel Specifics

• Hebrew prompts work well in Claude/GPT-4.
• Hebrew output format reliable.
• Privacy concerns — Israeli company data outside Israel.
• Cost in USD — currency consideration.

11. אסיים בהמלצה.

קלט נדרש

פלט צפוי

דגלים אדומים

• 🚨 AI for math — wrong results.
• 🚨 No output validation — garbage downstream.
• 🚨 Cost not monitored — bill shock.
• ⚠️ PII to LLM — privacy review needed.

הערות חשובות

• Haiku/GPT-4 mini for 80% of tasks.
• Json mode in Claude/GPT-4 for reliable parsing.
• Test prompts in Console first, then Module.

פרומפט לדוגמה

Classify support tickets ב-Make. Build with Claude.

Generate product descriptions ב-Zapier. Cost estimate.

AI hallucinations — איך לטפל?

© 2026 Automation Expert Pro | גרסה 1.0.0