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# Perplexity Model Comparison
Guide to different Perplexity models available through OpenRouter and when to use each.
## Available Models
All Perplexity models are accessed through OpenRouter using the format:
`openrouter/perplexity/[model-name]`
### Sonar Pro Search
**Model ID**: `openrouter/perplexity/sonar-pro-search`
**Best for:**
- Complex multi-step research questions
- Queries requiring deep analysis and synthesis
- Situations needing comprehensive source exploration
- Comparative analyses across multiple domains
- Research requiring agentic reasoning workflow
**Characteristics:**
- Most advanced agentic search system
- Executes multi-step reasoning workflows
- Uses tools and intermediate queries
- Provides most comprehensive answers
- Higher cost due to extensive processing
**Use cases:**
- "Conduct a comprehensive analysis of competing CAR-T cell therapy approaches, including mechanism differences, clinical outcomes, and cost-effectiveness"
- "Compare quantum computing approaches for drug discovery with traditional computational methods across multiple metrics"
- Research questions requiring synthesis from many sources
**Pricing** (approximate):
- Input: $3/million tokens
- Output: $15/million tokens
- Request fee: $18 per 1000 requests
**Context window**: 200K tokens
### Sonar Pro
**Model ID**: `openrouter/perplexity/sonar-pro`
**Best for:**
- General-purpose research and search
- Balanced performance and cost
- Standard scientific queries
- Quick information gathering
- Most use cases
**Characteristics:**
- Enhanced capabilities over base Sonar
- Good balance of quality and cost
- Reliable for most queries
- Faster than Pro Search
- Recommended default choice
**Use cases:**
- "What are the latest developments in CRISPR base editing?"
- "Summarize recent clinical trials for Alzheimer's treatment"
- "Explain how transformer architectures work in modern LLMs"
- Standard literature searches
- Technical documentation queries
**Pricing** (approximate):
- Lower cost than Pro Search
- Good cost-performance ratio
**Context window**: 200K tokens
### Sonar
**Model ID**: `openrouter/perplexity/sonar`
**Best for:**
- Basic searches and queries
- Cost-sensitive applications
- Simple fact-finding
- High-volume queries
- Quick lookups
**Characteristics:**
- Base model with solid performance
- Most cost-effective option
- Faster response times
- Good for straightforward queries
- Lower accuracy than Pro variants
**Use cases:**
- "What is the molecular weight of aspirin?"
- "When was CRISPR-Cas9 first used in humans?"
- "List the main symptoms of diabetes"
- Simple fact verification
- Basic information retrieval
**Pricing** (approximate):
- Lowest cost option
- Best for high-volume simple queries
**Context window**: 200K tokens
### Sonar Reasoning Pro
**Model ID**: `openrouter/perplexity/sonar-reasoning-pro`
**Best for:**
- Complex logical reasoning tasks
- Multi-step problem solving
- Technical analysis requiring step-by-step thinking
- Mathematical or computational problems
- Queries needing explicit reasoning chains
**Characteristics:**
- Advanced reasoning capabilities
- Shows step-by-step thinking
- Better for analytical tasks
- Excels at technical problem-solving
- More structured outputs
**Use cases:**
- "Walk through the steps to design a clinical trial for testing a novel cancer therapy"
- "Analyze the computational complexity of different protein folding algorithms"
- "Reason through the molecular mechanisms linking multiple genes to a disease phenotype"
- Technical troubleshooting with multiple steps
- Logical analysis of complex systems
**Pricing** (approximate):
- Higher cost due to reasoning capabilities
- Worth it for complex analytical tasks
**Context window**: 200K tokens
### Sonar Reasoning
**Model ID**: `openrouter/perplexity/sonar-reasoning`
**Best for:**
- Basic reasoning tasks
- Cost-effective analytical queries
- Simpler logical problems
- Step-by-step explanations
**Characteristics:**
- Basic reasoning capabilities
- More affordable than Reasoning Pro
- Good for moderate complexity tasks
- Shows logical thinking process
**Use cases:**
- "Explain the logic behind vaccine efficacy calculations"
- "Walk through basic statistical analysis steps"
- Simple analytical questions
- Educational explanations
**Pricing** (approximate):
- Lower cost than Reasoning Pro
- Good balance for basic reasoning
**Context window**: 200K tokens
## Model Selection Guide
### Decision Tree
```
Is your query complex and requiring deep multi-step analysis?
├─ YES → Use Sonar Pro Search
└─ NO → Continue
Does your query require explicit step-by-step reasoning?
├─ YES → Use Sonar Reasoning Pro (complex) or Sonar Reasoning (simple)
└─ NO → Continue
Is this a standard research or information query?
├─ YES → Use Sonar Pro (recommended default)
└─ NO → Continue
Is this a simple fact-finding or basic lookup?
├─ YES → Use Sonar (cost-effective)
└─ NO → Use Sonar Pro (safe default)
```
### By Use Case
| Use Case | Recommended Model | Alternative |
|----------|------------------|-------------|
| Literature review | Sonar Pro | Sonar Pro Search |
| Quick fact check | Sonar | Sonar Pro |
| Complex analysis | Sonar Pro Search | Sonar Reasoning Pro |
| Step-by-step tutorial | Sonar Reasoning Pro | Sonar Pro |
| Cost-sensitive bulk queries | Sonar | Sonar Pro |
| General research | Sonar Pro | Sonar |
| Technical debugging | Sonar Reasoning Pro | Sonar Pro |
| Comparative analysis | Sonar Pro Search | Sonar Pro |
### By Domain
**Biomedical Research:**
- Default: Sonar Pro
- Complex mechanisms: Sonar Reasoning Pro
- Literature synthesis: Sonar Pro Search
- Quick lookups: Sonar
**Computational Science:**
- Default: Sonar Pro
- Algorithm analysis: Sonar Reasoning Pro
- Technical docs: Sonar Pro
- Basic syntax: Sonar
**Drug Discovery:**
- Default: Sonar Pro
- Multi-target analysis: Sonar Pro Search
- Mechanism reasoning: Sonar Reasoning Pro
- Compound properties: Sonar
**Clinical Research:**
- Default: Sonar Pro
- Trial design: Sonar Reasoning Pro
- Evidence synthesis: Sonar Pro Search
- Basic guidelines: Sonar
## Performance Characteristics
### Response Time
**Fastest to Slowest:**
1. Sonar (fastest)
2. Sonar Pro
3. Sonar Reasoning
4. Sonar Reasoning Pro
5. Sonar Pro Search (slowest, due to multi-step processing)
**Considerations:**
- For time-sensitive queries, use Sonar or Sonar Pro
- For comprehensive analysis, accept the slower Sonar Pro Search
- Reasoning models are slower due to explicit thinking steps
### Quality vs Cost Trade-offs
**Quality Hierarchy** (highest to lowest):
1. Sonar Pro Search
2. Sonar Reasoning Pro
3. Sonar Pro
4. Sonar Reasoning
5. Sonar
**Cost Hierarchy** (most to least expensive):
1. Sonar Pro Search
2. Sonar Reasoning Pro
3. Sonar Pro
4. Sonar Reasoning
5. Sonar
**Recommendation**: Start with Sonar Pro as the default. Upgrade to Pro Search for complex queries, downgrade to Sonar for simple lookups.
### Accuracy and Comprehensiveness
**Most Comprehensive:**
- Sonar Pro Search: Explores multiple sources, synthesizes deeply
- Sonar Reasoning Pro: Thorough step-by-step analysis
**Most Accurate:**
- Sonar Pro Search: Best source verification and cross-checking
- Sonar Pro: Reliable for most queries
**Good Enough:**
- Sonar: Adequate for simple facts and basic queries
## Special Considerations
### Context Window
All models support 200K token context windows:
- Sufficient for most queries
- Can handle long documents or multiple sources
- Consider chunking very large analyses
### Temperature Settings
Different models benefit from different temperature settings:
**Sonar Pro Search:**
- Default: 0.2 (more focused, analytical)
- Use lower (0.0-0.1) for factual queries
- Use higher (0.3-0.5) for creative synthesis
**Sonar Reasoning Pro:**
- Default: 0.2
- Keep low (0.0-0.2) for logical consistency
- Reasoning quality degrades at high temperatures
**Sonar Pro / Sonar:**
- Default: 0.2
- Adjust based on query type (factual vs exploratory)
### Rate Limits and Quotas
OpenRouter enforces rate limits:
- Check your OpenRouter dashboard for current limits
- Consider request batching for high-volume use
- Monitor costs with OpenRouter's tracking tools
### API Key Security
**Best practices:**
- Never commit API keys to version control
- Use environment variables or .env files
- Rotate keys periodically
- Monitor usage for unexpected activity
- Use separate keys for different projects
## Example Comparisons
### Query: "Explain CRISPR-Cas9 gene editing"
**Sonar:**
- Quick overview
- Basic mechanism explanation
- ~200-300 tokens
- 1-2 sources cited
- Cost: $0.001
**Sonar Pro:**
- Detailed explanation
- Multiple mechanisms covered
- ~500-800 tokens
- 3-5 sources cited
- Cost: $0.003
**Sonar Reasoning Pro:**
- Step-by-step mechanism breakdown
- Logical flow of editing process
- ~800-1200 tokens
- Shows reasoning steps
- Cost: $0.005
**Sonar Pro Search:**
- Comprehensive analysis
- Multiple sources synthesized
- Historical context included
- Recent developments covered
- ~1500-2000 tokens
- 10+ sources explored
- Cost: $0.020+
### Query: "What is 2+2?"
All models return accurate answer. Use Sonar for simple queries to minimize cost.
### Query: "Design a clinical trial for novel immunotherapy"
**Sonar:**
- Basic template provided
- May miss important details
- Cost-effective but incomplete
**Sonar Pro:**
- Solid trial design framework
- Covers main components
- Good starting point
**Sonar Reasoning Pro:**
- Detailed step-by-step design
- Considers multiple factors
- Shows reasoning for each choice
- **Recommended for this query type**
**Sonar Pro Search:**
- Most comprehensive design
- Incorporates best practices from multiple sources
- Compares different approaches
- May be overkill for initial design
## Summary
**Default recommendation**: Start with **Sonar Pro** for most scientific queries.
**When to upgrade:**
- Complex multi-step analysis → Sonar Pro Search
- Explicit reasoning needed → Sonar Reasoning Pro
**When to downgrade:**
- Simple facts or lookups → Sonar
- Cost-sensitive bulk queries → Sonar
**Remember**: The best model depends on your specific use case, budget, and quality requirements. Monitor your usage and adjust model selection based on results.

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# OpenRouter Setup Guide
Complete guide to setting up and using OpenRouter for Perplexity model access.
## What is OpenRouter?
OpenRouter is a unified API gateway that provides access to 100+ AI models from various providers through a single API interface. It offers:
- **Single API key**: Access multiple models with one key
- **Unified format**: OpenAI-compatible API format
- **Cost tracking**: Built-in usage monitoring and billing
- **Model routing**: Intelligent fallback and load balancing
- **Pay-as-you-go**: No subscriptions, pay only for what you use
For Perplexity models specifically, OpenRouter provides exclusive access to certain models like Sonar Pro Search.
## Getting Started
### Step 1: Create OpenRouter Account
1. Visit https://openrouter.ai/
2. Click "Sign Up" in the top right
3. Sign up with Google, GitHub, or email
4. Verify your email if using email signup
### Step 2: Add Payment Method
OpenRouter uses pay-as-you-go billing:
1. Navigate to https://openrouter.ai/account
2. Click "Credits" tab
3. Add a payment method (credit card)
4. Add initial credits (minimum $5 recommended)
5. Optionally set up auto-recharge
**Pricing notes:**
- Models have different per-token costs
- See https://openrouter.ai/perplexity for Perplexity pricing
- Monitor usage at https://openrouter.ai/activity
### Step 3: Generate API Key
1. Go to https://openrouter.ai/keys
2. Click "Create Key"
3. Give your key a descriptive name (e.g., "perplexity-search-skill")
4. Optionally set usage limits for safety
5. Copy the key (starts with `sk-or-v1-...`)
6. **Important**: Save this key securely - you can't view it again!
**Security tips:**
- Never share your API key publicly
- Don't commit keys to version control
- Use separate keys for different projects
- Set usage limits to prevent unexpected charges
- Rotate keys periodically
### Step 4: Configure Environment
You have two options for setting up your API key:
#### Option A: Environment Variable (Recommended)
**Linux/macOS:**
```bash
export OPENROUTER_API_KEY='sk-or-v1-your-key-here'
```
To make it permanent, add to your shell profile:
```bash
# For bash: Add to ~/.bashrc or ~/.bash_profile
echo 'export OPENROUTER_API_KEY="sk-or-v1-your-key-here"' >> ~/.bashrc
source ~/.bashrc
# For zsh: Add to ~/.zshrc
echo 'export OPENROUTER_API_KEY="sk-or-v1-your-key-here"' >> ~/.zshrc
source ~/.zshrc
```
**Windows (PowerShell):**
```powershell
$env:OPENROUTER_API_KEY = "sk-or-v1-your-key-here"
```
To make it permanent:
```powershell
[System.Environment]::SetEnvironmentVariable('OPENROUTER_API_KEY', 'sk-or-v1-your-key-here', 'User')
```
#### Option B: .env File
Create a `.env` file in your project directory:
```bash
# Create .env file
cat > .env << EOF
OPENROUTER_API_KEY=sk-or-v1-your-key-here
EOF
```
Or use the setup script:
```bash
python scripts/setup_env.py --api-key sk-or-v1-your-key-here
```
Then load it before running scripts:
```bash
# Load environment variables from .env
source .env
# Or use python-dotenv
pip install python-dotenv
```
**Using python-dotenv in scripts:**
```python
from dotenv import load_dotenv
load_dotenv() # Loads .env file automatically
import os
api_key = os.environ.get("OPENROUTER_API_KEY")
```
### Step 5: Install Dependencies
Install LiteLLM using uv:
```bash
uv pip install litellm
```
Or with regular pip:
```bash
pip install litellm
```
**Optional dependencies:**
```bash
# For .env file support
uv pip install python-dotenv
# For additional features
uv pip install litellm[proxy] # If using LiteLLM proxy server
```
### Step 6: Verify Setup
Test your configuration:
```bash
# Using the setup script
python scripts/setup_env.py --validate
# Or using the search script
python scripts/perplexity_search.py --check-setup
```
You should see:
```
✓ OPENROUTER_API_KEY is set (sk-or-v1-...xxxx)
✓ LiteLLM is installed (version X.X.X)
✓ Setup is complete! You're ready to use Perplexity Search.
```
### Step 7: Test Your First Search
Run a simple test query:
```bash
python scripts/perplexity_search.py "What is CRISPR gene editing?"
```
Expected output:
```
================================================================================
ANSWER
================================================================================
CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a
revolutionary gene editing technology that allows precise modifications to DNA...
[detailed answer continues]
================================================================================
```
## Usage Monitoring
### Check Your Usage
Monitor your OpenRouter usage and costs:
1. Visit https://openrouter.ai/activity
2. View requests, tokens, and costs
3. Filter by date range, model, or key
4. Export usage data for analysis
### Set Usage Limits
Protect against unexpected charges:
1. Go to https://openrouter.ai/keys
2. Click on your key
3. Set "Rate limit" (requests per minute)
4. Set "Spending limit" (maximum total spend)
5. Enable "Auto-recharge" with limit if desired
**Recommended limits for development:**
- Rate limit: 10-20 requests per minute
- Spending limit: $10-50 depending on usage
### Cost Optimization
Tips for reducing costs:
1. **Choose appropriate models**: Use Sonar for simple queries, not Sonar Pro Search
2. **Set max_tokens**: Limit response length with `--max-tokens` parameter
3. **Batch queries**: Combine multiple simple questions when possible
4. **Monitor usage**: Check costs daily during heavy development
5. **Use caching**: Store results for repeated queries
## Troubleshooting
### Error: "OpenRouter API key not configured"
**Cause**: Environment variable not set
**Solution**:
```bash
# Check if variable is set
echo $OPENROUTER_API_KEY
# If empty, set it
export OPENROUTER_API_KEY='sk-or-v1-your-key-here'
# Or use setup script
python scripts/setup_env.py --api-key sk-or-v1-your-key-here
```
### Error: "Invalid API key"
**Causes**:
- Key was deleted or revoked
- Key has expired
- Typo in the key
- Wrong key format
**Solutions**:
1. Verify key at https://openrouter.ai/keys
2. Check for extra spaces or quotes
3. Generate a new key if needed
4. Ensure key starts with `sk-or-v1-`
### Error: "Insufficient credits"
**Cause**: OpenRouter account has run out of credits
**Solution**:
1. Go to https://openrouter.ai/account
2. Click "Credits" tab
3. Add more credits
4. Consider enabling auto-recharge
### Error: "Rate limit exceeded"
**Cause**: Too many requests in a short time
**Solutions**:
1. Wait a few seconds before retrying
2. Increase rate limit at https://openrouter.ai/keys
3. Implement exponential backoff in code
4. Batch requests or reduce frequency
### Error: "Model not found"
**Cause**: Incorrect model name or model no longer available
**Solution**:
1. Check available models at https://openrouter.ai/models
2. Use correct format: `openrouter/perplexity/sonar-pro`
3. Verify model is still supported
### Error: "LiteLLM not installed"
**Cause**: LiteLLM package is not installed
**Solution**:
```bash
uv pip install litellm
```
### Import Error with LiteLLM
**Cause**: Python path issues or version conflicts
**Solutions**:
1. Verify installation: `pip list | grep litellm`
2. Reinstall: `uv pip install --force-reinstall litellm`
3. Check Python version: `python --version` (requires 3.8+)
4. Use virtual environment to avoid conflicts
## Advanced Configuration
### Using Multiple Keys
For different projects or team members:
```bash
# Project 1
export OPENROUTER_API_KEY='sk-or-v1-project1-key'
# Project 2
export OPENROUTER_API_KEY='sk-or-v1-project2-key'
```
Or use .env files in different directories.
### Custom Base URL
If using OpenRouter proxy or custom endpoint:
```python
from litellm import completion
response = completion(
model="openrouter/perplexity/sonar-pro",
messages=[{"role": "user", "content": "query"}],
api_base="https://custom-endpoint.com/v1" # Custom URL
)
```
### Request Headers
Add custom headers for tracking:
```python
from litellm import completion
response = completion(
model="openrouter/perplexity/sonar-pro",
messages=[{"role": "user", "content": "query"}],
extra_headers={
"HTTP-Referer": "https://your-app.com",
"X-Title": "Your App Name"
}
)
```
### Timeout Configuration
Set custom timeouts for long-running queries:
```python
from litellm import completion
response = completion(
model="openrouter/perplexity/sonar-pro-search",
messages=[{"role": "user", "content": "complex query"}],
timeout=120 # 120 seconds timeout
)
```
## Security Best Practices
### API Key Management
1. **Never commit keys**: Add `.env` to `.gitignore`
2. **Use key rotation**: Rotate keys every 3-6 months
3. **Separate keys**: Different keys for dev/staging/production
4. **Monitor usage**: Check for unauthorized access
5. **Set limits**: Configure spending and rate limits
### .gitignore Template
Add to your `.gitignore`:
```
# Environment variables
.env
.env.local
.env.*.local
# API keys
*api_key*
*apikey*
*.key
# Sensitive configs
config/secrets.yaml
```
### Key Revocation
If a key is compromised:
1. Go to https://openrouter.ai/keys immediately
2. Click "Delete" on the compromised key
3. Generate a new key
4. Update all applications using the old key
5. Review usage logs for unauthorized access
6. Contact OpenRouter support if needed
## FAQs
**Q: How much does it cost to use Perplexity via OpenRouter?**
A: Pricing varies by model. Sonar is cheapest (~$0.001-0.002 per query), Sonar Pro is moderate (~$0.002-0.005), and Sonar Pro Search is most expensive (~$0.02-0.05+ per query). See https://openrouter.ai/perplexity for exact pricing.
**Q: Do I need a separate Perplexity API key?**
A: No! OpenRouter provides access to Perplexity models using only your OpenRouter key.
**Q: Can I use OpenRouter for other models besides Perplexity?**
A: Yes! OpenRouter provides access to 100+ models from OpenAI, Anthropic, Google, Meta, and more through the same API key.
**Q: Is there a free tier?**
A: OpenRouter requires payment, but offers very competitive pricing. Initial $5 credit should last for extensive testing.
**Q: How do I cancel my OpenRouter account?**
A: Contact OpenRouter support. Note that unused credits may not be refundable.
**Q: Can I use OpenRouter in production applications?**
A: Yes, OpenRouter is designed for production use with robust infrastructure, SLAs, and enterprise support available.
## Resources
**Official Documentation:**
- OpenRouter: https://openrouter.ai/docs
- Perplexity Models: https://openrouter.ai/perplexity
- LiteLLM: https://docs.litellm.ai/
**Account Management:**
- Dashboard: https://openrouter.ai/account
- API Keys: https://openrouter.ai/keys
- Usage: https://openrouter.ai/activity
- Billing: https://openrouter.ai/credits
**Community:**
- OpenRouter Discord: https://discord.gg/openrouter
- GitHub Issues: https://github.com/OpenRouter
- LiteLLM GitHub: https://github.com/BerriAI/litellm
## Summary
Setting up OpenRouter for Perplexity access involves:
1. Create account at https://openrouter.ai
2. Add payment method and credits
3. Generate API key at https://openrouter.ai/keys
4. Set `OPENROUTER_API_KEY` environment variable
5. Install LiteLLM: `uv pip install litellm`
6. Verify setup: `python scripts/setup_env.py --validate`
7. Start searching: `python scripts/perplexity_search.py "your query"`
Monitor usage and costs regularly to optimize your spending and ensure security.

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# Search Strategies for Perplexity
Best practices and strategies for crafting effective search queries with Perplexity models.
## Query Design Principles
### Be Specific and Detailed
Better results come from specific, well-structured queries rather than broad questions.
**Good examples:**
- "What are the latest clinical trial results for CAR-T cell therapy in treating B-cell lymphoma published in 2024?"
- "Compare the efficacy and safety profiles of mRNA vaccines versus viral vector vaccines for COVID-19"
- "Explain the mechanism of CRISPR-Cas9 off-target effects and current mitigation strategies"
**Bad examples:**
- "Tell me about cancer treatment" (too broad)
- "CRISPR" (too vague)
- "vaccines" (lacks specificity)
### Structure Complex Queries
Break complex questions into clear components:
1. **Topic**: What is the main subject?
2. **Scope**: What specific aspect are you interested in?
3. **Context**: What time frame, domain, or constraints apply?
4. **Output**: What format or type of answer do you need?
**Example:**
```
Topic: Protein folding prediction
Scope: AlphaFold3 improvements over AlphaFold2
Context: Published research from 2023-2024
Output: Technical comparison with specific accuracy metrics
```
**Query:**
"What improvements does AlphaFold3 offer over AlphaFold2 for protein structure prediction, according to research published between 2023 and 2024? Include specific accuracy metrics and benchmarks."
## Domain-Specific Search Patterns
### Scientific Literature Search
For scientific queries, include:
- Specific terminology and concepts
- Time constraints (recent publications)
- Methodology or study types of interest
- Journal quality or domain constraints
**Template:**
"What does recent research (2023-2024) say about [specific scientific concept] in [domain]? Focus on [peer-reviewed/preprint] studies and include [specific metrics/findings]."
**Example:**
"What does recent research (2023-2024) say about the role of gut microbiome in Parkinson's disease? Focus on peer-reviewed studies and include specific bacterial species identified."
### Technical/Engineering Search
For technical queries, specify:
- Technology stack or framework
- Use case or application context
- Version requirements
- Performance or implementation constraints
**Template:**
"How to [specific technical task] using [technology/framework] for [use case]? Include [implementation details/performance considerations]."
**Example:**
"How to implement real-time data streaming from Kafka to PostgreSQL using Python? Include considerations for handling backpressure and ensuring exactly-once semantics."
### Medical/Clinical Search
For medical queries, include:
- Specific conditions, treatments, or interventions
- Patient population or demographics
- Outcomes of interest
- Evidence level (RCTs, meta-analyses, etc.)
**Template:**
"What is the evidence for [intervention] in treating [condition] in [population]? Focus on [study types] and report [specific outcomes]."
**Example:**
"What is the evidence for intermittent fasting in managing type 2 diabetes in adults? Focus on randomized controlled trials and report HbA1c changes and weight loss outcomes."
## Advanced Query Techniques
### Comparative Analysis
For comparing multiple options:
**Template:**
"Compare [option A] versus [option B] for [use case] in terms of [criteria 1], [criteria 2], and [criteria 3]. Include [specific evidence or metrics]."
**Example:**
"Compare PyTorch versus TensorFlow for implementing transformer models in terms of ease of use, performance, and ecosystem support. Include benchmarks from recent studies."
### Trend Analysis
For understanding trends over time:
**Template:**
"What are the key trends in [domain/topic] over the past [time period]? Highlight [specific aspects] and include [data or examples]."
**Example:**
"What are the key trends in single-cell RNA sequencing technology over the past 5 years? Highlight improvements in throughput, cost, and resolution, with specific examples."
### Gap Identification
For finding research or knowledge gaps:
**Template:**
"What are the current limitations and open questions in [field/topic]? Focus on [specific aspects] and identify areas needing further research."
**Example:**
"What are the current limitations and open questions in quantum error correction? Focus on practical implementations and identify scalability challenges."
### Mechanism Explanation
For understanding how things work:
**Template:**
"Explain the mechanism by which [process/phenomenon] occurs in [context]. Include [level of detail] and discuss [specific aspects]."
**Example:**
"Explain the mechanism by which mRNA vaccines induce immune responses. Include molecular details of translation, antigen presentation, and memory cell formation."
## Query Refinement Strategies
### Start Broad, Then Narrow
1. **Initial query**: "Recent developments in cancer immunotherapy"
2. **Refined query**: "Recent developments in checkpoint inhibitor combination therapies for melanoma"
3. **Specific query**: "What are the clinical trial results for combining anti-PD-1 and anti-CTLA-4 checkpoint inhibitors in metastatic melanoma patients, published 2023-2024?"
### Add Constraints Iteratively
Start with core query, then add constraints:
1. **Base**: "Machine learning for drug discovery"
2. **Add domain**: "Machine learning for small molecule drug discovery"
3. **Add method**: "Deep learning approaches for small molecule property prediction"
4. **Add context**: "Recent deep learning approaches (2023-2024) for predicting ADMET properties of small molecules, including accuracy benchmarks"
### Specify Desired Output Format
Improve answers by specifying the output format:
- "Provide a step-by-step explanation..."
- "Summarize in bullet points..."
- "Create a comparison table of..."
- "List the top 5 approaches with pros and cons..."
- "Include specific numerical benchmarks and metrics..."
## Common Pitfalls to Avoid
### Too Vague
**Problem**: "Tell me about AI"
**Solution**: "What are the current state-of-the-art approaches for few-shot learning in computer vision as of 2024?"
### Loaded Questions
**Problem**: "Why is drug X better than drug Y?"
**Solution**: "Compare the efficacy and safety profiles of drug X versus drug Y based on clinical trial evidence."
### Multiple Unrelated Questions
**Problem**: "What is CRISPR and how do vaccines work and what causes cancer?"
**Solution**: Ask separate queries for each topic.
### Assumed Knowledge Without Context
**Problem**: "What are the latest results?" (Latest results for what?)
**Solution**: "What are the latest clinical trial results for CAR-T cell therapy in treating acute lymphoblastic leukemia?"
## Domain-Specific Keywords
### Biomedical Research
Use precise terminology:
- "randomized controlled trial" instead of "study"
- "meta-analysis" instead of "review"
- "in vitro" vs "in vivo" vs "clinical"
- "peer-reviewed" for quality filter
- Specific gene/protein names (e.g., "BRCA1" not "breast cancer gene")
### Computational/AI Research
Use technical terms:
- "transformer architecture" not "AI model"
- "few-shot learning" not "learning from limited data"
- "zero-shot" vs "few-shot" vs "fine-tuning"
- Specific model names (e.g., "GPT-4" not "language model")
### Chemistry/Drug Discovery
Use IUPAC names and specific terms:
- "small molecule" vs "biologic"
- "pharmacokinetics" (ADME) vs "pharmacodynamics"
- Specific assay types (e.g., "IC50", "EC50")
- Drug names (generic vs brand)
## Time-Constrained Searches
Perplexity searches real-time web data, making time constraints valuable:
**Templates:**
- "What papers were published in [journal] in [month/year] about [topic]?"
- "What are the latest developments (past 6 months) in [field]?"
- "What was announced at [conference] [year] regarding [topic]?"
- "What are the most recent clinical trial results (2024) for [treatment]?"
**Examples:**
- "What papers were published in Nature Medicine in January 2024 about long COVID?"
- "What are the latest developments (past 6 months) in large language model training efficiency?"
- "What was announced at NeurIPS 2023 regarding AI safety and alignment?"
## Source Quality Considerations
For high-quality results, mention source preferences:
- "According to peer-reviewed publications..."
- "Based on clinical trial registries like clinicaltrials.gov..."
- "From authoritative sources such as Nature, Science, Cell..."
- "According to FDA/EMA approvals..."
- "Based on systematic reviews or meta-analyses..."
**Example:**
"What is the current understanding of microplastics' impact on human health according to peer-reviewed research published in high-impact journals since 2022?"
## Iterative Search Workflow
For comprehensive research:
1. **Broad overview**: Get general understanding
2. **Specific deep-dives**: Focus on particular aspects
3. **Comparative analysis**: Compare approaches/methods
4. **Latest updates**: Find most recent developments
5. **Critical evaluation**: Identify limitations and gaps
**Example workflow for "CAR-T cell therapy":**
1. "What is CAR-T cell therapy and how does it work?"
2. "What are the specific molecular mechanisms by which CAR-T cells recognize and kill cancer cells?"
3. "Compare first-generation, second-generation, and third-generation CAR-T cell designs"
4. "What are the latest clinical trial results for CAR-T therapy in treating solid tumors (2024)?"
5. "What are the current limitations and challenges in CAR-T cell therapy, and what approaches are being investigated to address them?"
## Summary
Effective Perplexity searches require:
1. **Specificity**: Clear, detailed queries
2. **Structure**: Well-organized questions with context
3. **Terminology**: Domain-appropriate keywords
4. **Constraints**: Time frames, sources, output formats
5. **Iteration**: Refine based on initial results
The more specific and structured your query, the better and more relevant your results will be.