Key Takeaways
- Test with your actual data, not benchmarks: general benchmarks diverge from real-world performance. Build a test set of 50-100 representative queries.
- Start with the cheapest model that meets quality requirements: upgrade only when testing proves the cheaper model insufficient.
- Design for model switching: abstract the model layer so you can swap providers as the landscape evolves and prices change.
- Fine-tuning is usually unnecessary: 90% of use cases achieve target quality through prompt engineering alone. Fine-tune only as a last resort.
- Consider total cost of ownership: API costs, implementation time, maintenance, and opportunity cost of model limitations.
LLM Evaluation Framework
- Define requirements: What tasks? What accuracy level? What latency? What data privacy constraints? What monthly budget?
- Identify 3-4 candidate models: mix of premium (GPT-4, Claude Sonnet), mid-tier (GPT-4o, Gemini Pro), and economy (Llama 3, Mistral, Haiku)
- Build a test dataset: 50-100 representative inputs with expected outputs from your actual business context
- Evaluate systematically: score each model on accuracy, latency, cost, and qualitative output quality
- Run a pilot: deploy the top candidate for 2-4 weeks with real users before committing
Decision Matrix by Business Priority
| Priority | Recommended Model | Reasoning |
|---|---|---|
| Maximum quality | GPT-4 or Claude Opus | Highest capability across all tasks |
| Customer-facing safety | Claude Sonnet | Constitutional AI, fewer harmful outputs |
| Cost optimization | Llama 3 (self-hosted) or Gemini Flash | Lowest per-token cost |
| EU data compliance | Mistral (EU) or self-hosted Llama | Data stays in Europe |
| Speed-critical | Groq + Llama 3 | 10-18x faster inference |
| Google ecosystem | Gemini Pro (Vertex AI) | Native Workspace integration |
| Microsoft ecosystem | Azure OpenAI (GPT-4) | Native M365 integration |
| Maximum data privacy | Self-hosted Llama/Mistral | Nothing leaves your servers |
| Broadest capability | GPT-4 Turbo | Largest ecosystem, most integrations |
Common LLM Selection Mistakes
- Choosing based on hype, not testing: every model has marketing claims. Only your test data reveals real-world performance for your use case.
- Over-investing in the most expensive model: GPT-4 at $30/MTok output is 60x more expensive than Haiku at $0.50/MTok. Many tasks do not need GPT-4.
- Ignoring operational costs: self-hosted models have zero API costs but require infrastructure, monitoring, and engineering time.
- Locking into one provider: the LLM landscape changes rapidly. Design your architecture for easy model switching.
- Fine-tuning too early: prompt engineering improvements are cheaper, faster, and more maintainable than fine-tuning. Exhaust prompt optimization first.
Avoid Vendor Lock-In
The LLM landscape will look very different in 12 months. Prices will drop, new models will launch, and capabilities will shift. Build a model-agnostic architecture from day one. The 2-3 hours of abstraction work saves weeks of migration work later.
Architecture for Model Flexibility
Design your integration to support easy model switching:
- LLM abstraction layer: create a service interface that accepts prompts and returns responses. Implementation details (provider, model, parameters) are configuration, not code.
- Prompt templates: store prompts separately from code. Different models may need different prompt styles for the same task.
- Evaluation pipeline: automated testing against your test dataset whenever you change models or prompts. Catch regressions before production.
- Cost tracking: monitor per-request costs by model and use case. This data drives optimization decisions.
Frequently Asked Questions
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