LLM Best Practices
Choose the right LLM, optimize costs, ensure safety, evaluate with benchmarks, and build production LLM applications.
Choosing the Right LLM
Match the model to your requirements:
| Requirement | Recommended Approach |
|---|---|
| Best quality, any cost | GPT-4o, Claude 4, Gemini 2.0 Pro |
| Best quality/cost ratio | Claude 3.5 Haiku, GPT-4o-mini, Gemini 2.0 Flash |
| Fastest inference | Groq (LLaMA 3), Gemini Flash, Claude Haiku |
| Data privacy required | Self-hosted LLaMA 3, Mistral, Qwen |
| Custom domain expertise | Fine-tuned open model (LoRA on LLaMA/Mistral) |
| Long documents | Gemini (1M+), Claude (200K) |
| Code generation | Claude 4, GPT-4o, DeepSeek Coder |
| Budget-constrained | Together AI, Groq, or local Ollama |
Cost Optimization
- Model routing: Route simple queries to cheap models and complex ones to expensive models. Use a classifier or heuristics to determine complexity.
- Prompt optimization: Shorter prompts = fewer input tokens = lower cost. Remove redundant instructions.
- Caching: Cache responses for identical or semantically similar queries. Semantic caching can save 30-50% of API costs.
- Batch APIs: Use batch endpoints (50% cheaper at OpenAI) for non-real-time workloads.
- Output length limits: Set max_tokens to prevent models from generating unnecessarily long responses.
- Prompt caching: Anthropic and OpenAI offer prompt caching for repeated system prompts (up to 90% savings).
Latency Optimization
- Use streaming: Stream tokens to show partial responses immediately. Users perceive faster responses.
- Choose faster models: Smaller models (Haiku, Flash, GPT-4o-mini) have lower latency.
- Reduce context: Less input = faster time-to-first-token.
- Use Groq: Their LPU hardware offers the fastest inference for open models.
- Pre-compute: Pre-generate responses for common queries during off-peak hours.
Safety and Content Filtering
- Input validation: Filter and sanitize user inputs before sending to the LLM.
- Output filtering: Check model outputs for harmful, biased, or inappropriate content before showing to users.
- Prompt injection defense: Use system prompts to instruct the model to ignore attempts to override its instructions. Use input/output guards.
- Rate limiting: Prevent abuse by limiting requests per user/IP.
- Logging and monitoring: Log all interactions for audit and to detect misuse patterns.
Evaluation Benchmarks
| Benchmark | What It Tests | Key Models Tested |
|---|---|---|
| MMLU | General knowledge across 57 subjects | All major models |
| HumanEval | Code generation (Python functions) | Code-focused models |
| MT-Bench | Multi-turn conversation quality (GPT-4 judge) | Chat models |
| GPQA | Graduate-level science questions | Frontier models |
| Arena ELO | Human preference ratings (Chatbot Arena) | All chat models |
| GSM8K | Grade school math word problems | All major models |
| MATH | Competition math problems | Reasoning models |
| SWE-bench | Real-world software engineering tasks | Code agents |
Keeping Up with New Models
- Follow the LMSYS Chatbot Arena leaderboard for real human preference rankings.
- Check Hugging Face Open LLM Leaderboard for open model comparisons.
- Read announcements from OpenAI, Anthropic, Google, Meta, and Mistral blogs.
- Join communities: r/LocalLLaMA, Hugging Face Discord, AI Twitter/X.
Building LLM-Powered Applications
Start with prompting
Before building complex systems, get the prompts right. 80% of the value often comes from good prompt engineering.
Add retrieval (RAG)
When the model needs current or domain-specific knowledge, add a vector database and retrieval pipeline.
Implement guardrails
Add input/output validation, content filtering, and error handling before going to production.
Build evaluation
Create a test suite of queries with expected outputs. Automate evaluation to catch regressions.
Iterate on the model
Try different models, fine-tune if needed, and continuously improve based on user feedback.
Frequently Asked Questions
Start with Claude 3.5 Haiku or GPT-4o-mini for prototyping (fast, cheap, good quality). Upgrade to Claude 4 or GPT-4o for production if you need maximum quality. Use open models (LLaMA 3, Mistral) if you need privacy, customization, or want to avoid vendor dependency. Always benchmark on your specific use case.
Yes, thousands of companies use LLMs in production. Key requirements: implement proper error handling, set up monitoring and logging, add content filtering, handle API failures gracefully, and always have a fallback. Never rely on an LLM for safety-critical decisions without human oversight.
Use RAG to ground responses in factual documents. Add instructions like "Only answer based on the provided context. If you don't know, say so." Use lower temperature settings (0.0-0.3) for factual tasks. Implement fact-checking pipelines for critical applications. Consider using multiple models and checking for consistency.
The gap has been narrowing rapidly. LLaMA 3 405B is competitive with GPT-4 on many benchmarks. DeepSeek V3 and Qwen 2.5 have shown that open models can match frontier capabilities at specific sizes. For many practical applications, open models are already "good enough." The gap at the very frontier will likely persist but continue to shrink.
Costs vary enormously. A simple chatbot handling 1,000 queries/day with GPT-4o-mini might cost $5-15/month. A high-volume application processing 100,000 documents/day with GPT-4o could cost $3,000-10,000/month. Self-hosting a 70B model on 2x A100 GPUs costs roughly $5,000-8,000/month for the hardware. Always prototype with cheap models and estimate production costs before committing.
Ready to Go Deeper?
Live instructor-led courses from our partners. Affiliate disclosure.
AI & ML Courses - 30% Off
Live instructor-led AI, machine learning, data science, and cloud courses for working professionals. Use code Limited30 at checkout.
EdurekaDataCamp - AI & Data Science
Hands-on Python, machine learning, and AI courses with interactive exercises and real projects.
DataCampedX - Top AI Courses
University-level AI courses from MIT, Harvard, Stanford. Earn certificates that employers recognize.
edX