AI Systems Guide
Understand the AI models and systems powering PromptReports. Learn how to select, configure, and optimize AI providers for your research and report generation needs.
About AI Systems#
PromptReports leverages multiple state-of-the-art AI models through OpenRouter.ai, giving you access to a wide range of large language models (LLMs) for different use cases. Understanding these systems helps you make informed decisions about which models to use for your specific research and report generation needs.
Our platform abstracts the complexity of working with multiple AI providers, allowing you to easily switch between models, compare outputs, and optimize for quality, speed, or cost depending on your requirements.
Multi-Model Access
Access dozens of leading AI models from a single platform without managing multiple API keys.
Unified Interface
Consistent experience across all models with standardized prompting and output handling.
Easy Switching
Switch between models with a single click to compare results and find the best fit.
Enterprise Security
All API communications are encrypted and processed according to enterprise security standards.
OpenRouter Integration
Available Models#
We provide access to a variety of AI models optimized for different tasks:
| Provider | Model Family | Strengths | Best For |
|---|---|---|---|
| OpenAI | GPT-4, GPT-4 Turbo | Reasoning, instruction following, versatility | Complex analysis, report generation |
| Anthropic | Claude 3 (Opus, Sonnet, Haiku) | Long context, nuanced writing, safety | Research synthesis, detailed reports |
| Gemini Pro, Gemini Ultra | Multimodal, factual accuracy | Data analysis, fact-checking | |
| Meta | Llama 3, Code Llama | Open-source, coding, customization | Technical reports, code generation |
| Mistral | Mistral Large, Medium, Small | Efficiency, multilingual | Cost-effective tasks, European focus |
| Cohere | Command R+ | RAG, enterprise search | Research with citations |
Flagship Models
Top-tier models like GPT-4 and Claude 3 Opus for highest quality outputs.
Fast Models
Quick-response models for real-time applications and rapid iteration.
Specialized Models
Purpose-built models for specific tasks like coding or embedding.
Model Availability
Model Selection Guide#
Choosing the right model depends on your specific use case. Consider these factors:
| Use Case | Recommended Models | Why |
|---|---|---|
| In-depth research reports | Claude 3 Opus, GPT-4 | Best reasoning and synthesis capabilities |
| Quick analysis & summaries | Claude 3 Sonnet, GPT-4 Turbo | Good balance of quality and speed |
| High-volume processing | Claude 3 Haiku, Mistral Small | Cost-effective for batch operations |
| Technical/code content | GPT-4, Code Llama | Strong code understanding and generation |
| Multilingual content | GPT-4, Mistral Large | Excellent non-English language support |
| Real-time applications | Claude 3 Haiku, GPT-3.5 Turbo | Lowest latency response times |
Define your requirements
Start with a flagship model
Test alternatives
Compare outputs
Optimize for production
Configuration Options#
Fine-tune model behavior with these configuration parameters:
| Parameter | Range | Effect | Recommendation |
|---|---|---|---|
| Temperature | 0.0 - 2.0 | Controls randomness/creativity | 0.3-0.7 for reports, higher for brainstorming |
| Max Tokens | 1 - model limit | Maximum response length | Set based on expected output length |
| Top P | 0.0 - 1.0 | Nucleus sampling threshold | Usually keep at default (1.0) |
| Frequency Penalty | -2.0 - 2.0 | Reduces word repetition | 0.0-0.5 for varied outputs |
| Presence Penalty | -2.0 - 2.0 | Encourages new topics | 0.0-0.3 for comprehensive coverage |
Model Settings
Configure default parameters for each model in your account settings.
Preset Configurations
Save and reuse parameter configurations for different use cases.
Context Windows
Understand each model's context limit to manage long inputs effectively.
System Prompts
Customize model behavior with system-level instructions.
Context Limits
Performance & Costs#
Understanding performance characteristics helps optimize your usage:
| Model Tier | Relative Cost | Typical Latency | Use When |
|---|---|---|---|
| Flagship (GPT-4, Claude Opus) | High | 5-30 seconds | Quality is critical |
| Standard (GPT-4 Turbo, Claude Sonnet) | Medium | 2-10 seconds | Good balance needed |
| Economy (GPT-3.5, Claude Haiku) | Low | 0.5-3 seconds | Speed or cost is priority |
| Open Source (Llama, Mistral) | Very Low | 1-5 seconds | Budget-constrained projects |
Usage Tracking
Monitor your AI usage and costs in real-time through the dashboard.
Cost Optimization
Use cheaper models for drafts, expensive models for final outputs.
Cost optimization strategies:
- Use faster, cheaper models for iteration and testing during development
- Reserve flagship models for production and final report generation
- Implement caching for repeated queries to avoid redundant API calls
- Optimize prompt length to reduce token usage without sacrificing quality
- Batch similar requests together when possible for efficiency
- Set appropriate max token limits to avoid unnecessarily long responses
Best Practices#
Maximize the effectiveness of AI systems with these recommendations:
Clear Instructions
Provide specific, unambiguous prompts. Models perform best with clear guidance.
Structured Outputs
Request outputs in structured formats (JSON, markdown) for easier processing.
Iterative Refinement
Start with simple prompts and iterate based on outputs.
Validate Outputs
Always review AI outputs for accuracy, especially for factual claims.
| Do | Avoid |
|---|---|
| Provide context and examples | Assuming the model knows your domain |
| Specify output format explicitly | Leaving format to interpretation |
| Break complex tasks into steps | Asking for too much in one prompt |
| Test across multiple models | Assuming one model fits all tasks |
| Monitor for quality regression | Set and forget configurations |
| Use system prompts for consistent behavior | Repeating instructions in every user prompt |