Performance Tuning | Twig

Optimize your AI agents for speed, accuracy, and cost-effectiveness.

Performance Metrics

Track in Analytics dashboard:

Metric	Target	Measured How
p50 Latency	<2s	50th percentile response time
p95 Latency	<4s	95th percentile (worst-case normal)
p99 Latency	<6s	99th percentile (outliers)
Accuracy Rate	>85%	% responses marked accurate
Cost/Query	<$0.01	Total API costs / query count
Cache Hit Rate	>40%	Cached / total queries

Location: Dashboard → Analytics → Performance tab

Optimization Tradeoffs

Cannot maximize all three simultaneously:

Speed ⟷ Accuracy ⟷ Cost

Pick 2:

Speed + Cost → Redwood + GPT-3.5-turbo (accuracy: ~72%)
Speed + Accuracy → Cedar + GPT-4 + cache (cost: medium)
Accuracy + Cost → Cypress + GPT-4o-mini (speed: ~3-5s)

Speed Optimization

1. Choose Faster RAG Strategy

Strategy	Avg Latency	Best For
Redwood	~1.2s	Maximum speed
Cedar	~2.0s	Balanced
Cypress	~3.5s	Maximum accuracy

Switch to Redwood when:

Questions are clear and direct
Speed is critical
High query volume

2. Reduce topK

// Higher topK = slower
topK: 20  → Response time: 2.5s
topK: 10  → Response time: 1.8s
topK: 5   → Response time: 1.2s

Recommendation: Start with 5-7, increase only if accuracy suffers.

3. Use Faster Model

Model	Speed	Quality	Cost
GPT-3.5-turbo	Fast	Good	Low
GPT-4o-mini	Fast	Better	Low
GPT-4o	Medium	Excellent	High
GPT-4	Slow	Excellent	High

For speed: Use GPT-3.5-turbo for simple queries, GPT-4o for complex.

4. Enable Caching

{
  "cache": {
    "enabled": true,
    "ttl": 300,              // 5 minutes
    "keyBy": ["prompt", "agentId"]
  }
}

Impact: 50-100ms for cached responses vs 1-3s for uncached.

5. Optimize Context

// Reduce context size
{
  "topK": 5,               // Fewer documents
  "maxContextTokens": 2000, // Limit context size
  "chunkSize": 300         // Smaller chunks
}

6. Use Streaming

// User sees response immediately
stream: true

// First token: ~500ms
// Complete response: ~2s
// Perceived latency: Much faster

Accuracy Optimization

1. Choose Better RAG Strategy

Cypress > Cedar > Redwood for accuracy.

2. Increase topK

topK: 5   → Accuracy: 85%
topK: 10  → Accuracy: 89%
topK: 20  → Accuracy: 91%

Diminishing returns after topK ~15.

3. Use Better Model

GPT-4o or GPT-4 for highest quality.

4. Improve Instructions

// Detailed system prompt
instructions: `
You are an expert [domain] assistant.

When answering:
1. Always cite specific sources
2. Provide step-by-step explanations
3. Include code examples when relevant
4. Verify facts against documentation
5. Admit uncertainty when appropriate
`

5. Add High-Quality Data Sources

✅ Official documentation ✅ Verified knowledge base ✅ Recent, updated content ❌ Low-quality, outdated content

6. Enable Reranking (Cypress)

Reranking improves precision by 20-30%.

7. Use Private Data Only

configAIUseOnlyPrivateData: true

Prevents hallucination from general knowledge.

Cost Optimization

1. Choose Cost-Effective Model

Model	Cost per 1M Tokens
GPT-3.5-turbo	$0.50
GPT-4o-mini	$0.15
GPT-4o	$5.00
GPT-4	$30.00

Recommendation: GPT-4o-mini for most use cases.

2. Reduce Token Usage

{
  "maxTokens": 300,        // Limit response length
  "topK": 5,               // Fewer documents
  "memoryTurns": 3,        // Less conversation history
  "temperature": 0.3       // More focused (fewer tokens)
}

3. Aggressive Caching

{
  "cache": {
    "enabled": true,
    "ttl": 3600,           // 1 hour (longer cache)
    "fuzzyMatching": true   // Match similar queries
  }
}

4. Use Redwood Strategy

Redwood is cheapest (single LLM call, no reranking).

5. Batch Operations

Process multiple queries together to reduce overhead.

6. Smart Routing

def route_query(query):
    if is_simple(query):
        return "REDWOOD"      # Cheapest
    elif is_complex(query):
        return "CYPRESS"      # Worth the cost
    else:
        return "CEDAR"        # Balanced

Balanced Optimization

The Performance Triangle

        Speed
         /\
        /  \
       /    \
      /      \
     /________\
  Cost      Accuracy

You can optimize 2 of 3:

Speed + Cost: Use Redwood, GPT-3.5-turbo
Speed + Accuracy: Use Cedar, GPT-4o, caching
Cost + Accuracy: Use Cypress, efficient models, batch

Recommended Configurations

High-Volume Support Bot:

Strategy: REDWOOD
Model: gpt-3.5-turbo
topK: 5
Cache: enabled (1 hour)
Goal: Handle 10k+ queries/day cheaply

Technical Documentation:

Strategy: CEDAR
Model: gpt-4o
topK: 10
Cache: enabled (30 min)
Goal: Balance speed and accuracy

Compliance Assistant:

Strategy: CYPRESS
Model: gpt-4o
topK: 10
Reranking: enabled
Goal: Maximum accuracy, cost secondary

Performance Monitoring

Key Metrics Dashboard

Agent: Customer Support
├─ Avg Response Time: 1.8s (target: <2s) ✅
├─ P95 Response Time: 2.4s (target: <3s) ✅
├─ P99 Response Time: 3.1s ⚠️
├─ Cache Hit Rate: 42%
├─ Avg Tokens: 1,523
├─ Cost per Query: $0.0045
└─ Accuracy Score: 89%

Set Performance Targets

{
  "targets": {
    "responseTime": {
      "p50": 1.5,
      "p95": 2.5,
      "p99": 4.0
    },
    "accuracy": 0.85,
    "costPerQuery": 0.01,
    "cacheHitRate": 0.40
  }
}

Alerting

{
  "alerts": {
    "responseTimeSlow": {
      "threshold": 3.0,
      "duration": "5m",
      "notify": "team@company.com"
    },
    "accuracyDrop": {
      "threshold": 0.80,
      "compare": "baseline",
      "notify": "team@company.com"
    }
  }
}

A/B Testing

Compare configurations to find optimal settings:

// Test A: Baseline
const configA = {
  strategyCode: 'CEDAR',
  topK: 10,
  temperature: 0.7
};

// Test B: Optimized for speed
const configB = {
  strategyCode: 'REDWOOD',
  topK: 5,
  temperature: 0.7
};

// Route 50% traffic to each
// Measure: speed, accuracy, cost
// Deploy winner after 1 week

Continuous Optimization

Weekly Review

Check performance metrics
Identify bottlenecks
Test optimizations
Deploy improvements
Measure impact

Monthly Audit

Review all configurations
Benchmark against baselines
Update targets
Plan next optimizations

Tools & Techniques

Performance Profiling

const startTime = Date.now();

const response = await twig.chat.create({
  prompt,
  agentId,
  profile: true  // Enable profiling
});

console.log('Breakdown:', {
  embedding: response.profile.embeddingTime,
  retrieval: response.profile.retrievalTime,
  llm: response.profile.llmTime,
  total: Date.now() - startTime
});

Load Testing

# Using Apache Bench
ab -n 1000 -c 10 -H "Authorization: Bearer KEY" \
  -p query.json https://api.twig.so/api/chat

# Results show:
# - Requests per second
# - Average latency
# - P50, P95, P99

Cache Analysis

const cacheStats = await twig.cache.stats();

console.log('Hit rate:', cacheStats.hitRate);
console.log('Avg savings:', cacheStats.avgTimeSaved);
console.log('Most cached:', cacheStats.topQueries);

Next Steps

Cost Optimization - Reduce expenses
Analytics Dashboard - Monitor metrics
RAG Strategies - Choose optimal strategy
Evaluation Framework - Measure quality

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Key Takeaways