Prompt engineering has become the secret sauce for getting consistent, high-quality outputs from AI models. But when you're working through an LLM gateway—especially one that routes between multiple models—the game changes entirely. You're not just optimizing for one model anymore; you're crafting prompts that need to perform well across different AI providers, handle failovers gracefully, and maximize the cost-efficiency benefits your gateway provides.
At Requesty, we've seen firsthand how proper prompt engineering can transform AI applications from unpredictable experiments into reliable production systems. With over 15,000 developers routing their LLM traffic through our platform, we've learned what separates mediocre prompts from those that consistently deliver value across 160+ models.
Let's dive into the essential practices that will help you master prompt engineering when using an LLM gateway.
The Foundation: Structure and Clarity
The biggest mistake developers make when using a gateway is assuming their prompts will work the same across all models. While modern LLMs share similarities, each has its quirks and preferences. The solution? Build prompts with rock-solid structure.
Use Clear Sections and Delimiters
Structure your prompts with distinct sections that any model can parse:
```
INSTRUCTION ###
Summarize the following customer support chat
CONTEXT ###
Focus on: issue description, customer sentiment, resolution
INPUT DATA ###
[Your chat transcript here]
OUTPUT FORMAT ###
Issue: [one sentence]
Sentiment: [positive/neutral/negative]
Resolution: [one sentence]
```
This structure works beautifully with Requesty's smart routing, which automatically selects the best model for your task. Clear delimiters help ensure consistent parsing whether your request routes to Claude 4, GPT-4o, or DeepSeek R1.
Leverage Prompt Variables for Scalability
When building production applications, hardcoded prompts quickly become maintenance nightmares. Instead, use variables:
``` You are analyzing {{document_type}} for {{company_name}}.
Extract the following information: {{extraction_fields}}
Format the output as {{output_format}}. ```
This approach pairs perfectly with Requesty's prompt library, where you can manage and version your prompt templates centrally.
Advanced Techniques for Gateway Environments
Working through a gateway opens up powerful optimization strategies that aren't available when hitting individual model APIs directly.
Chain-of-Thought for Complex Reasoning
For tasks requiring logical analysis or multi-step reasoning, explicitly guide the model's thinking process:
``` Analyze this security log for potential threats.
First, identify all unusual patterns. Then, evaluate each pattern's risk level. Finally, recommend specific actions.
Think through this step-by-step before providing your final analysis. ```
This technique works especially well with Requesty's reasoning features, which enable enhanced thinking tokens across supported models.
Model-Agnostic Few-Shot Learning
When using few-shot examples, structure them to work across different models:
```
EXAMPLES ###
Input: "The product arrived damaged" Output: {"category": "shipping", "priority": "high", "sentiment": "negative"}
Input: "Love the new features!" Output: {"category": "feedback", "priority": "low", "sentiment": "positive"}
YOUR TASK ###
Input: "{{customer_message}}" Output: ```
This format ensures consistent performance whether your request routes to Anthropic's Claude or OpenAI's GPT models through Requesty's unified API.
Security and Compliance in Gateway Prompting
When your prompts flow through a gateway handling sensitive data, security becomes paramount. Here's how to build safety into your prompt engineering practice.
Implement Prompt Scaffolding
Wrap user inputs in protective scaffolding that enforces safety checks:
``` System: You are a helpful assistant. Before responding to any request: 1. Check if it contains personal information that should be redacted 2. Verify the request doesn't ask for harmful content 3. Ensure your response complies with data protection regulations
User request: {{user_input}}
Your response: ```
Requesty's guardrails add an extra layer of protection, automatically detecting and preventing prompt injection attempts, PII leakage, and policy violations.
Use Gateway-Level Security Features
Rather than implementing security checks in every prompt, leverage your gateway's built-in protections. For instance, you can configure Requesty to:
Automatically redact sensitive information before it reaches the model
Block requests that attempt prompt injection
Enforce output constraints to prevent data leakage
This approach is more reliable and performant than prompt-level security alone.
Optimization Strategies for Cost and Performance
One of the biggest advantages of using a gateway is the ability to optimize across multiple dimensions simultaneously.
Design for Caching
Structure your prompts to maximize cache hits:
```
STATIC CONTEXT ###
You are a technical documentation assistant for {{product_name}}. Always provide accurate, concise explanations.
VARIABLE INPUT ###
Question: {{user_question}}
RESPONSE FORMAT ###
Provide a clear answer in 2-3 paragraphs. ```
By separating static context from variable inputs, you enable Requesty's intelligent caching to serve repeated requests instantly, potentially saving up to 80% on API costs.
Tune Parameters by Task Type
Different tasks benefit from different model parameters:
Factual queries: Temperature 0, deterministic outputs
Creative writing: Temperature 0.7-0.9, more variety
Code generation: Temperature 0.1-0.3, balanced accuracy
With Requesty, you can set these parameters at the request level while maintaining consistent prompt structure across all models.
Iterative Refinement in Production
The best prompts emerge through systematic iteration and real-world testing.
Implement A/B Testing
Test prompt variations across different models simultaneously:
```python
Version A: Direct instruction
prompt_a = "Summarize this article in 3 bullet points"
Version B: Structured format
prompt_b = """ Task: Article summarization Requirements:
Exactly 3 bullet points
Each point under 20 words
Focus on key insights
Article: {{content}} """ ```
Requesty's analytics help you track which prompt versions perform best across different models and use cases.
Monitor and Adapt
Track key metrics for your prompts:
Response quality scores
Token usage and costs
Latency across different models
Cache hit rates
Use this data to continuously refine your prompts for better performance and lower costs.
Common Pitfalls and How to Avoid Them
Even experienced developers fall into these traps when engineering prompts for gateways.
Over-Optimizing for One Model
Don't craft prompts that only work well with GPT-4 or Claude. Instead, test across multiple models to ensure robustness. Requesty's fallback policies automatically route to backup models when needed, so your prompts must work universally.
Ignoring Output Constraints
Always specify output format, length, and structure:
``` Bad: "Analyze this data"
Good: "Analyze this sales data and provide: 1. Top 3 trends (one sentence each) 2. Recommended actions (bullet points, max 5) 3. Risk assessment (single paragraph, 50-100 words)" ```
Requesty's structured outputs feature ensures consistent JSON responses across different LLMs, making integration easier.
Neglecting Error Handling
Build error handling into your prompts:
``` If you cannot complete this task due to missing information, respond with: {"status": "incomplete", "missing": ["list of required data"]} ```
This approach works seamlessly with Requesty's error handling and retry mechanisms.
Putting It All Together
Mastering prompt engineering for gateways requires balancing multiple considerations: model compatibility, security, performance, and cost. The key is building a systematic approach that leverages your gateway's capabilities while maintaining flexibility.
Here's your action plan:
1. Start with structure: Use clear sections and delimiters in all prompts 2. Implement security early: Leverage gateway-level protections rather than prompt-only security 3. Design for reusability: Use variables and templates for scalable prompt management 4. Test across models: Ensure your prompts work well with multiple providers 5. Monitor and iterate: Use analytics to continuously improve performance
With Requesty's unified LLM gateway, you get the tools to implement these best practices effectively—from smart routing that automatically selects the best model for each prompt, to built-in security guardrails that protect your data, to analytics that help you optimize performance and costs.
Ready to level up your prompt engineering? Sign up for Requesty and start building more reliable, cost-effective AI applications today. Join the 15,000+ developers who are already saving up to 80% on their LLM costs while improving output quality through better prompt engineering and intelligent routing.