Matching LLMs to Use Cases

Let's map common business use cases to the right LLM choices. This practical guide will help you make informed decisions.

Customer Support Chatbot

Requirements: Fast responses, handles FAQs, escalates complex issues

Recommended approach:

• First tier: Claude Haiku 3.5 or GPT-3.5 Turbo
  • Handles 80% of simple queries
  • Fast, cost-effective
• Escalation tier: Claude Sonnet 4 or GPT-4o
  • Complex issues, unhappy customers
  • Better reasoning and empathy

Key consideration: Implement conversation memory and handoff to humans.

Document Analysis & Summarization

Requirements: Process long documents, extract key information

Recommended approach:

• Long documents (50+ pages): Claude 4 (200K context)
  • Can process entire documents at once
  • No chunking needed
• Standard documents: Any capable model
  • GPT-4o (128K context)
  • Gemini 1.5 Pro (1M context)

Key consideration: Test with your actual document types.

Code Generation & Review

Requirements: Write, explain, debug code

Recommended approach:

• Production code: GPT-4o or Claude Sonnet 4/Opus 4.5
  • Better code quality and explanations
  • Understands complex architectures
• Quick prototypes: GPT-3.5 or Claude Haiku 3.5
  • Fast iteration
  • Good for boilerplate code

Key consideration: Always review AI-generated code before production use.

Content Creation

Requirements: Marketing copy, blog posts, social media

Recommended approach:

• Brand-critical content: Claude Opus 4.5 or GPT-4o
  • Better nuance and creativity
  • Follows brand guidelines well
• High-volume content: GPT-3.5 or Claude Haiku 3.5
  • Drafts that humans will edit
  • Social media variations

Key consideration: Always have human review for published content.

Data Extraction & Structuring

Requirements: Convert unstructured text to structured data

Recommended approach:

• Complex extraction: GPT-4o with JSON mode
  • Reliable structured output
  • Handles edge cases well
• Simple patterns: Any model with clear examples
  • Few-shot prompting works well
  • Lower cost at scale

Key consideration: Validate outputs against expected schema.

Decision Matrix

Use Case	Speed Priority	Quality Priority	Budget Priority
Customer Support	Haiku/3.5	Sonnet/GPT-4o	Haiku/3.5
Document Analysis	Sonnet	Opus/GPT-4o	Self-hosted
Code Generation	Haiku	Opus/GPT-4o	Self-hosted
Content Creation	3.5/Haiku	Opus/GPT-4o	3.5/Haiku
Data Extraction	3.5/Haiku	GPT-4o JSON	Self-hosted

Getting Started Recommendation

1. Start with a balanced model (GPT-4o mini or Claude Sonnet 4)
2. Measure quality and cost for your specific use case
3. Experiment down to cheaper models where quality holds
4. Scale up only for tasks that clearly need it

Remember: The best model is the one that solves your problem at acceptable cost and quality—not necessarily the most powerful one.

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