Choosing the right large language model for your application is crucial for success. This guide provides a framework for evaluating and selecting LLMs based on your specific needs.
Model performance varies significantly based on your specific use case. Always benchmark with your actual data before making a final decision.
interface ModelEvaluation {
accuracy: number; // Task-specific accuracy
latency: number; // Response time in ms
throughput: number; // Tokens per second
costPerToken: number; // Pricing efficiency
contextWindow: number; // Maximum input length
}
Test each model on representative tasks:
def evaluate_model(model, test_cases):
results = []
for case in test_cases:
start_time = time.time()
response = model.generate(case.prompt)
latency = time.time() - start_time
results.append({
'accuracy': score_response(response, case.expected),
'latency': latency,
'cost': calculate_cost(case.prompt, response)
})
return aggregate_results(results)
Token costs can quickly add up in production. Implement smart truncation and caching strategies to control expenses.
function optimizePrompt(text: string, maxTokens: number): string {
const tokens = tokenize(text);
if (tokens.length <= maxTokens) return text;
// Smart truncation preserving important context
return truncateIntelligently(tokens, maxTokens);
}
const responseCache = new Map<string, CachedResponse>();
function getCachedResponse(prompt: string): string | null {
const hash = hashPrompt(prompt);
const cached = responseCache.get(hash);
if (cached && !isExpired(cached)) {
return cached.response;
}
return null;
}
async function robustLLMCall(prompt: string, options: LLMOptions) {
const models = ['gpt-4', 'claude-3', 'gemini-pro'];
for (const model of models) {
try {
return await callModel(model, prompt, options);
} catch (error) {
console.warn(`Model ${model} failed: ${error.message}`);
continue;
}
}
throw new Error('All models failed');
}
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Track key metrics in production:
Selecting the right LLM requires balancing performance, cost, and reliability. Use this framework to systematically evaluate options and make data-driven decisions for your specific use case.