Token Optimization Techniques

Executive Summary

Token optimization has become a critical competency for organizations deploying LLM applications at scale. With AI workloads projected to exceed $840 billion by 2026, and output tokens costing 3-10x more than input tokens, systematic optimization strategies are no longer optional—they're essential for sustainable AI operations.

1. Token Economics and Pricing (2026)

Understanding the Output Token Premium

The fundamental asymmetry in LLM pricing—where output tokens cost 2-10x more than input tokens—creates the primary opportunity for optimization. This premium reflects the computational cost of generation versus processing.

Current Pricing Landscape (2026)

Major Provider Comparison

Prompt Caching Pricing

Reasoning Tokens Cost Impact

ROI Calculations for Optimization

Understanding the financial impact of optimization strategies requires modeling realistic usage patterns:

2. Prompt Compression Techniques

Overview of Compression Approaches

Prompt compression reduces the length and complexity of input prompts while preserving semantic meaning and task performance. Three main categories of techniques have emerged:

1. Hard Prompt Methods (Token Removal)

These methods remove redundant tokens from natural language prompts while retaining semantic meaning:

• SelectiveContext: Uses lexical analysis to identify and preserve key tokens
• LLMLingua: Leverages smaller language models to rank and preserve essential tokens, achieving up to 20x compression^{1AcademicLLMLingua-2: Data Distillation for Efficient Prompt CompressionPan et al., ACL 2024View Paper}
• LongLLMLingua: Optimized variant for long-context scenarios with question-aware coarse-to-fine compression^{2AcademicLongLLMLingua: Accelerating and Enhancing LLMs in Long Context ScenariosJiang et al., ACL 2024View Paper}
• AdaComp: Adaptive compression with dynamic compression ratios

2. Soft Prompt Methods (Embedding-Based)

These methods encode prompts into continuous trainable embeddings or key-value pairs:

• AutoCompressor/ICAE: Learns compressed representations through training, compressing long contexts into compact "memory slots" with 4x compression at 90%+ performance^{4AcademicIn-context Autoencoder for Context Compression in a Large Language ModelGe et al., ICLR 2024View Paper}
• GIST: Generalizable soft prompt compression, compressing prompts into "gist" tokens achieving up to 26x compression^{3AcademicLearning to Compress Prompts with Gist TokensMu et al., NeurIPS 2023View Paper}
• 500xCompressor: Achieves extreme compression ratios up to 480x
• xRAG: Specialized for RAG applications

3. Traditional Techniques

• Semantic Summarization: Condenses long/repetitive content while retaining essential semantics
• Relevance Filtering: Measures relevance and includes only the most pertinent context
• Keyphrase Extraction: Identifies and preserves critical keywords and phrases
• Semantic Chunking: Intelligently segments based on semantic boundaries

Performance and Cost Savings

Real-World Implementation Examples

System: You are a helpful customer service agent for Acme Corp.
Our company values excellent customer service. We sell widgets,
gadgets, and various products. Our return policy allows returns
within 30 days. We offer free shipping on orders over $50.
Customer satisfaction is our top priority. We have a dedicated
support team available 24/7 to assist with any questions or
concerns. Our products come with a one-year warranty...

[Additional 600 tokens of context and instructions]

User: How do I return a defective widget?

customer service Acme Corp. return policy 30 days
free shipping $50. support 24/7. warranty one-year

User: return defective widget?

Industry Case Study: LinkedIn

Compression Tools and Libraries

Best Practices for Prompt Compression

1. Start with semantic summarization for immediate 30-50% gains with minimal risk
2. Test on representative samples before deploying aggressive compression
3. Monitor quality metrics to ensure compression doesn't degrade performance
4. Use domain-specific compression when possible for better preservation of critical terms
5. Combine with caching to maximize ROI (compress once, cache repeatedly)
6. Consider task complexity — simple tasks tolerate more compression than complex reasoning

3. Context Caching Strategies

Understanding Prompt Caching

Prompt caching allows LLM providers to store and reuse frequently accessed context, offering dramatic cost reductions (50-90%) and latency improvements (up to 85%) for applications with repeated context. This is particularly valuable for:

• RAG applications with stable knowledge bases
• Agents with consistent system prompts and tool definitions
• Applications processing multiple queries against the same documents
• Multi-turn conversations with long context

Anthropic Claude Prompt Caching

Implementation Structure

Claude's prompt caching^{7IndustryPrompt Caching with ClaudeAnthropic Documentation, 2024View Docs} processes the prompt in order: tools → system → messages, marking cacheable sections with the cache_control parameter.

Cache Configuration Options

Implementation Example

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-5-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are an AI assistant for Acme Corp...",
        },
        {
            "type": "text",
            "text": "Here is our product catalog: [10,000 tokens of product data]",
            "cache_control": {"type": "ephemeral"}  # Cache this section
        }
    ],
    messages=[
        {"role": "user", "content": "What's the price of widget XYZ?"}
    ]
)

Key Requirements and Constraints

• Minimum cache size: 1,024 tokens per cache checkpoint (Claude 3.7+)
• Maximum breakpoints: Up to 4 cache breakpoints per prompt
• Supported models: Claude Opus 4.1/4, Sonnet 4.5/4/3.7, Haiku 4.5/3.5/3
• Cache lifetime: 5 minutes default, 1 hour optional (refreshed on each use)

OpenAI Prompt Caching

Automatic Caching Mechanism

OpenAI's approach^{8IndustryPrompt Caching in the APIOpenAI Platform, 2024View Docs} is simpler—it automatically caches the longest prefix of prompts that have been previously computed. No API changes required.

Caching Behavior

• Minimum cache size: Starts at 1,024 tokens, increments in 128-token blocks
• Cache matching: Requires exact prefix match (byte-level identical)
• Cache lifetime: 5-10 minutes of inactivity, maximum 1 hour total
• Discount: Flat 50% on cached input tokens

Optimization Strategies for Maximum Cache Utilization

1. Structure Prompts for Cacheability

// Variable content first
User query: "What is product X?"

// Static content last (not cached)
System: Long instructions...
Tools: [tool definitions...]
Knowledge: [product catalog...]

// Static content first (cached)
Tools: [tool definitions...]
System: Long instructions...
Knowledge: [product catalog...]

// Variable content last
User query: "What is product X?"

2. Batch Similar Requests

Process multiple queries against the same context within the cache TTL window to maximize cache utilization:

# Batch-related queries within 5-minute window
questions = [
    "What's the price of widget A?",
    "Does widget B come with warranty?",
    "Compare widgets C and D"
]

# Process rapidly to hit cache
for question in questions:
    response = client.messages.create(
        model="claude-sonnet-4-5",
        system=[...cached_context...],
        messages=[{"role": "user", "content": question}]
    )
    # 90% cost reduction on cached tokens after first call

3. Combine Documents into Single Cached Blocks

4. Monitor Cache Hit Rates

Track cache performance to optimize your implementation:

• Monitor cache hit rates in API responses (Anthropic provides cache metadata)
• Target 70%+ cache hit rate for cost-effective caching
• Break-even point: ~4-5 cache reads per write (5-min cache, 1.25x write cost)
• Adjust cache TTL based on usage patterns (1-hour cache for sustained workloads)

Cost Analysis: Caching ROI

4. Intelligent Model Routing

The Model Routing Opportunity

For 70-80% of production workloads, mid-tier models perform identically to premium models. Intelligent routing directs simple queries to cost-effective models while reserving expensive frontier models for complex reasoning tasks.

Routing Strategies

1. Complexity-Based Routing

Analyze query complexity and route accordingly:

2. Cascade Routing (Try Smaller First)

Attempt with smaller model first, escalate only on failure:

async def cascade_route(query, context):
    # Try Haiku first (cheap, fast)
    haiku_response = await call_haiku(query, context)

    # Check confidence/quality
    if haiku_response.confidence > 0.85:
        return haiku_response  # 70% of queries stop here

    # Escalate to Sonnet for medium complexity
    sonnet_response = await call_sonnet(query, context)

    if sonnet_response.confidence > 0.90:
        return sonnet_response  # 25% need this tier

    # Final escalation to Opus for complex tasks
    return await call_opus(query, context)  # Only 5% reach here

3. Semantic Routing (Intent-Based)

Use lightweight classifier to determine query intent and route accordingly:

from semantic_router import Route, Router

# Define routes
faq_route = Route(
    name="faq",
    model="claude-haiku-4-5",
    utterances=["hours", "location", "contact", "price"]
)

support_route = Route(
    name="support",
    model="claude-sonnet-4-5",
    utterances=["problem", "not working", "error", "help"]
)

analysis_route = Route(
    name="analysis",
    model="claude-opus-4-5",
    utterances=["compare", "analyze", "evaluate", "recommend"]
)

router = Router(routes=[faq_route, support_route, analysis_route])
route = router.classify(user_query)  # Fast, cheap classification
response = await call_model(route.model, user_query)

Real-World Routing Impact

Router Implementation Frameworks

Routing Best Practices

1. Start with conservative routing: Begin with 50/50 split, measure quality, then adjust
2. Monitor quality metrics closely: Track accuracy, user satisfaction per route
3. Account for latency: Routing adds 5-20ms overhead—ensure it's acceptable
4. Use confidence scores: Let models self-report confidence for cascade routing
5. Combine with caching: Cache routing decisions for repeated queries
6. A/B test routing strategies: Compare different routing algorithms in production
7. Avoid over-optimization: Don't route to reasoning models (o1/o3) unless absolutely necessary

5. RAG Token Optimization

The RAG Token Challenge

Retrieval-Augmented Generation (RAG) applications face a unique token challenge: retrieved context often constitutes 70-90% of total input tokens. Optimizing RAG pipelines can deliver 50-80% cost reduction while improving accuracy.

Advanced Chunking Strategies

Why Chunking Matters

Chunking determines how documents are segmented for indexing and retrieval. Poor chunking leads to:

• Broken semantic coherence (mid-sentence splits)
• Excessive retrieval (retrieving too many irrelevant chunks)
• Context overflow (exceeding token limits)
• Cost inflation (unnecessary tokens in context)

Chunking Strategy Comparison (2026 Research)

Implementation: Semantic Chunking

from langchain.text_splitter import SemanticChunker
from langchain.embeddings import OpenAIEmbeddings

# Semantic chunking based on embedding similarity
embeddings = OpenAIEmbeddings()
text_splitter = SemanticChunker(
    embeddings=embeddings,
    breakpoint_threshold_type="percentile",  # Use percentile-based threshold
    breakpoint_threshold_amount=95,  # Split at 95th percentile similarity
    number_of_chunks=None  # Let it determine optimal chunk count
)

# Process documents
chunks = text_splitter.create_documents([long_document])

# Result: Semantically coherent chunks (avg 200-400 tokens)
# vs fixed-size chunks that often break mid-thought

Hybrid Retrieval for Token Efficiency

Combine multiple retrieval methods to maximize relevance while minimizing retrieved tokens:

Hybrid Retrieval Architecture

from langchain.retrievers import EnsembleRetriever
from langchain.vectorstores import FAISS
from langchain.retrievers import BM25Retriever

# Dense retrieval (semantic search)
vectorstore = FAISS.from_documents(chunks, embeddings)
dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Sparse retrieval (keyword-based)
sparse_retriever = BM25Retriever.from_documents(chunks)
sparse_retriever.k = 5

# Hybrid ensemble (weighted combination)
ensemble_retriever = EnsembleRetriever(
    retrievers=[dense_retriever, sparse_retriever],
    weights=[0.6, 0.4]  # Favor semantic, but include keyword matches
)

# Retrieve most relevant chunks (better precision = fewer tokens)
relevant_docs = ensemble_retriever.get_relevant_documents(query)

Re-ranking for Context Compression

Re-ranking reduces retrieved context by 50-70% while improving relevance:

Re-ranker Implementation

from sentence_transformers import CrossEncoder

# Load cross-encoder re-ranker
reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

# Initial retrieval (cast wide net)
initial_docs = retriever.get_relevant_documents(query, k=20)

# Re-rank based on query-document relevance
pairs = [[query, doc.page_content] for doc in initial_docs]
scores = reranker.predict(pairs)

# Sort by score and keep top-k
ranked_docs = [doc for _, doc in sorted(
    zip(scores, initial_docs),
    key=lambda x: x[0],
    reverse=True
)][:5]  # Keep only top 5 most relevant

# Result: 75% fewer tokens, higher relevance

RAG-Specific Compression

xRAG: Compression for RAG

xRAG specializes in compressing retrieved context while preserving answer-critical information:

• Identifies and preserves answer-bearing passages
• Removes redundant information across chunks
• Compresses supporting context more aggressively than key facts
• Achieves 60-80% compression with <5% accuracy loss

Complete RAG Optimization Stack

RAG Optimization Best Practices

1. Test chunk sizes empirically: 100-300 tokens works well for most tasks, but domain-specific tuning is critical
2. Never hardcode chunking strategy: A/B test multiple approaches with your specific data
3. Implement hybrid retrieval: Combining dense + sparse consistently outperforms either alone
4. Always re-rank: Re-ranking provides the best ROI for token reduction (75% savings, minimal complexity)
5. Cache knowledge bases: Static knowledge bases are perfect caching candidates
6. Monitor retrieval quality: Track precision@k and recall@k to ensure optimization doesn't degrade relevance
7. Consider task-specific embeddings: Fine-tuned embeddings improve retrieval quality, reducing over-retrieval

6. Batch Processing and BatchPrompt

Batch API Discounts

Both OpenAI and Anthropic offer significant discounts for batch processing—50% off standard pricing for workloads that can tolerate 24-hour processing windows.

When to Use Batch Processing

Continuous Batching for Self-Hosted Models

Continuous batching dynamically groups requests for maximum GPU utilization:

Traditional Static Batching vs. Continuous Batching

BatchPrompt: The Research Technique

Core Concept

BatchPrompt batches multiple data points in each prompt to improve efficiency. Instead of making 100 API calls for 100 items, make 10 calls with 10 items each.

Basic BatchPrompt Example

for review in reviews:  # 100 reviews
    prompt = f"Classify sentiment: {review}"
    result = llm.call(prompt)
    # Cost: 100 API calls
    # Latency: 100 * 2s = 200s

batches = chunk(reviews, 10)  # 10 batches of 10
for batch in batches:
    prompt = f"Classify sentiment for each:\n"
    for i, review in enumerate(batch):
        prompt += f"{i+1}. {review}\n"
    results = llm.call(prompt)
    # Cost: 10 API calls (90% reduction)
    # Latency: 10 * 3s = 30s (85% faster)

Advanced: BPE + SEAS

The BatchPrompt paper introduces two sophisticated techniques:

BPE (Batch Permutation and Ensembling)

Runs multiple voting rounds with different data orderings to improve accuracy:

def bpe_classify(batch, num_rounds=3):
    votes = defaultdict(list)

    for round in range(num_rounds):
        # Shuffle batch order each round
        shuffled = random.sample(batch, len(batch))

        # Get predictions for this ordering
        predictions = batch_prompt(shuffled)

        # Collect votes
        for item_id, prediction in predictions.items():
            votes[item_id].append(prediction)

    # Majority vote
    return {id: Counter(v).most_common(1)[0][0]
            for id, v in votes.items()}

SEAS (Self-reflection-guided Early Stopping)

Stops voting early for confident predictions:

def seas_classify(batch, confidence_threshold=0.9):
    votes = defaultdict(list)
    confidences = defaultdict(list)
    completed = set()

    for round in range(max_rounds):
        # Skip completed items
        active_batch = [item for item in batch
                       if item.id not in completed]

        if not active_batch:
            break

        # Get predictions with confidence scores
        results = batch_prompt_with_confidence(active_batch)

        for item_id, (pred, conf) in results.items():
            votes[item_id].append(pred)
            confidences[item_id].append(conf)

            # Early stop if confident
            if conf > confidence_threshold and len(votes[item_id]) >= 2:
                if votes[item_id][-1] == votes[item_id][-2]:
                    completed.add(item_id)

    return {id: Counter(v).most_common(1)[0][0]
            for id, v in votes.items()}

BatchPrompt Performance (Research Results)

Batch Processing Best Practices

1. Use provider batch APIs for async workloads: 50% instant discount for 24-hour processing window
2. Implement BatchPrompt for classification tasks: 80%+ cost reduction with BPE+SEAS
3. Tune batch size: 10-32 items per batch optimal for most tasks
4. Combine with caching: Batch similar queries to maximize cache hits
5. Monitor quality: Larger batches can degrade accuracy—find the sweet spot
6. Use continuous batching for self-hosted: 2-4x throughput improvement

7. Token-Efficient Prompting Patterns

Zero-Shot vs. Few-Shot Trade-offs (2026 Research)

When to Use Each Approach

Optimized Prompting Patterns

Pattern 1: Zero-Shot CoT (Most Token-Efficient)

Example 1: [Complex example with reasoning steps]
[200 tokens]

Example 2: [Another complex example]
[200 tokens]

Example 3: [Third example]
[200 tokens]

Now solve this problem:
[User query]

[User query]

Let's think step by step.

Pattern 2: Concise Few-Shot (Format Only)

Extract key information in JSON format.

Example:
Input: "John Smith, age 35, lives in NYC"
Output: {"name": "John Smith", "age": 35, "city": "NYC"}

Now process: [user input]

~50 tokens vs. 200+ for verbose examples. Use for format specification only.

Pattern 3: Auto-CoT (Automatic Example Generation)

Automatically generate diverse, simple examples instead of manual curation:

• Choose demonstrations with <60 tokens
• Select rationales with <5 reasoning steps
• Prioritize simpler questions
• Ensure diversity across examples

Structured Output Optimization

Use Native Structured Output APIs

Both OpenAI and Anthropic now offer structured output modes that eliminate the need for verbose format instructions:

Extract information and return in JSON format.
Use the following schema:
{
  "name": "string",
  "age": "integer",
  "email": "string",
  "interests": ["string"]
}

Ensure all fields are present. Use null if
unknown. Validate email format. Return only
valid JSON, no explanations.

response = client.messages.create(
    model="claude-sonnet-4-5",
    response_format={
        "type": "json_schema",
        "json_schema": schema
    },
    messages=[...]
)

# Schema enforced by API
# No format instructions needed

Reasoning Model Optimization

Token-Efficient Prompting Checklist

1. Use Zero-Shot CoT with strong models instead of Few-Shot CoT (96% token savings)
2. ✅ Use structured output APIs instead of verbose format instructions (100-200 token savings)
3. ✅ Provide minimal, concise examples for format alignment only (~50 tokens vs. 200+)
4. ✅ Remove unnecessary pleasantries and verbose instructions ("please", "kindly", etc.)
5. ✅ Use schema-aware formatting (JSON schema, TypeScript types) instead of natural language descriptions
6. ✅ Cache static instructions and system prompts (90% savings on repeated context)
7. ✅ Avoid reasoning models (o1/o3) unless absolutely necessary (10-30x cost multiplier)
8. ✅ Test zero-shot before adding examples (often performs equally well with frontier models)

8. Cost Monitoring and Observability Tools

Why Cost Monitoring Is Critical

75% of enterprises adopt FinOps automation by 2026, shifting from reactive cost control to autonomous optimization with AI agents managing AI costs. Real-time cost tracking enables:

• Early detection of cost anomalies (runaway loops, prompt injection attacks)
• Per-user, per-feature cost attribution
• ROI measurement for optimization strategies
• Budget alerts and guardrails
• Model performance vs. cost trade-off analysis

Leading LLM Observability Platforms (2026)

Key Metrics to Track

Cost Metrics

• Cost per request: Average cost across all requests
• Cost per user: Identify high-cost users or abuse patterns
• Cost per feature: Which features drive costs (RAG, reasoning, chat)?
• Input/output token ratio: Monitor for unexpected shifts
• Cache hit rate: Track caching effectiveness (target >70%)
• Model distribution: Percentage routed to each model tier

Performance Metrics

• Latency (p50, p95, p99): Response time distribution
• Throughput: Requests per second
• Error rate: Failed requests, rate limits, timeouts
• Token usage per request: Detect prompt bloat

Quality Metrics

• Groundedness: Responses grounded in retrieved context (RAG)
• Relevance: Response relevance to prompt
• Hallucination rate: Factually incorrect outputs
• User satisfaction: Thumbs up/down, feedback scores

Implementation: OpenTelemetry-Based Tracking

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Setup OpenTelemetry
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
otlp_exporter = OTLPSpanExporter(endpoint="your-collector:4317")
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(otlp_exporter)
)

# Instrument LLM calls
@tracer.start_as_current_span("llm_call")
def call_llm(prompt, model="claude-sonnet-4-5"):
    span = trace.get_current_span()

    # Add metadata
    span.set_attribute("llm.model", model)
    span.set_attribute("llm.prompt_tokens", len(prompt.split()))

    response = client.messages.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )

    # Track usage and cost
    span.set_attribute("llm.input_tokens", response.usage.input_tokens)
    span.set_attribute("llm.output_tokens", response.usage.output_tokens)
    span.set_attribute("llm.cache_read_tokens",
                      response.usage.cache_read_input_tokens or 0)

    # Calculate cost
    cost = calculate_cost(response.usage, model)
    span.set_attribute("llm.cost", cost)

    return response

Cost Alerting and Guardrails

Budget Alerts

# Set up cost alerting
class CostGuardrail:
    def __init__(self, daily_budget=100.0):
        self.daily_budget = daily_budget
        self.current_spend = 0.0
        self.reset_daily()

    def check_budget(self, estimated_cost):
        if self.current_spend + estimated_cost > self.daily_budget:
            raise BudgetExceededError(
                f"Request would exceed daily budget: "
                f"${self.current_spend + estimated_cost:.2f} > ${self.daily_budget}"
            )
        return True

    def record_spend(self, actual_cost):
        self.current_spend += actual_cost

        # Alert at 80% threshold
        if self.current_spend > self.daily_budget * 0.8:
            send_alert(f"Daily budget 80% consumed: ${self.current_spend:.2f}")

# Use in application
guardrail = CostGuardrail(daily_budget=100.0)

def process_request(user_input):
    estimated_cost = estimate_cost(user_input)
    guardrail.check_budget(estimated_cost)

    response = call_llm(user_input)
    guardrail.record_spend(response.cost)

    return response

Observability Best Practices

1. Instrument from day one: Cost tracking should be in place before production deployment
2. Use OpenTelemetry: Industry standard for tracing, portable across platforms
3. Track quality + cost together: Cost optimization without quality tracking leads to degraded experiences
4. Set up automated alerts: Budget thresholds, anomaly detection, error rate spikes
5. Implement per-user tracking: Identify abuse, support tiered pricing, understand usage patterns
6. Monitor cache performance: Track hit rates, ensure caching ROI is positive
7. Review dashboards weekly: Regular review identifies optimization opportunities

9. Implementation Roadmap

Phased Optimization Approach

Implement token optimization in stages to manage complexity and measure impact:

Phase 1: Quick Wins (Week 1-2)

Phase 2: Strategic Optimization (Week 3-6)

Phase 3: Advanced Optimization (Week 7-12)

Success Criteria and Measurement

ROI Calculator

Estimate your potential savings:

Common Pitfalls to Avoid

1. Optimizing without monitoring: You can't improve what you don't measure. Deploy observability first.
2. Over-compressing prompts: Aggressive compression can degrade quality. Test thoroughly.
3. Ignoring cache TTL: Low cache hit rates make caching cost-ineffective. Monitor and adjust.
4. Routing too aggressively: Over-routing to small models increases error rates and user frustration.
5. Forgetting quality metrics: Cost optimization without quality tracking leads to poor user experience.
6. Skipping A/B tests: Always compare optimized vs. baseline in production before full rollout.
7. Optimizing too early: Premature optimization wastes time. Wait until you have meaningful usage data.

10. Key Takeaways

Essential Optimization Techniques (Ranked by ROI)

The Token Optimization Mindset

2026 Market Context

As AI workloads are projected to exceed $840 billion by 2026, token optimization has become a core competency for AI teams. Organizations that master these techniques gain significant competitive advantages through:

• Lower operational costs: 60-80% cost reduction enables broader AI deployment
• Faster response times: Optimized prompts reduce latency
• Improved quality: Focused context often improves accuracy
• Sustainable scaling: Cost-effective operations enable growth
• Competitive pricing: Lower costs enable more aggressive pricing strategies