Common LLM Pitfalls and Best Practices

Input-Related Pitfalls

Prompt Engineering

• Ambiguous Instructions - Unclear or vague prompts lead to unreliable outputs
• Context Length Limits - Exceeding token limits causes truncation
• Missing Context - Insufficient background information for accurate responses
• Prompt Injection - Malicious inputs that override intended behavior
• Jailbreaking - Attempts to bypass model’s safety measures
• Direct Prompting - Explicitly asking for harmful content
• Indirect Prompting - Using creative ways to extract unwanted behavior

Data Quality

• Inconsistent Formatting - Varying data structures causing parsing errors
• Incomplete Information - Missing crucial details for task completion
• Biased Training Data - Inherent biases affecting model outputs
• Data Hallucination - Generation of false or inaccurate information

Output-Related Pitfalls

Response Quality

• Inaccurate Information - Factually incorrect or outdated responses
• Inconsistent Outputs - Varying responses for similar inputs
• Format Violations - Responses not following specified formats
• Incomplete Answers - Partial or truncated responses
• Confabulation - Making up information to complete gaps
• False Confidence - High confidence in incorrect answers
• Source Attribution - Inability to cite reliable sources

Bias and Fairness

• Demographic Bias - Unfair treatment based on demographics
• Representation Bias - Underrepresentation of certain groups
• Language Bias - Favoring certain linguistic patterns
• Cultural Bias - Western-centric or culturally insensitive outputs
• Historical Bias - Reflecting historical prejudices
• Algorithmic Bias - Systematic errors in model architecture

Mitigation Strategies

Input Protection

• Implement input sanitization
• Use system prompts for constraints
• Apply content filtering
• Monitor for injection attempts

Output Verification

• Fact-checking mechanisms
• Cross-reference with reliable sources
• Multiple model consensus
• Human-in-the-loop validation

Bias Detection and Control

• Regular bias audits
• Diverse training data
• Bias measurement metrics
• Feedback collection systems

Technical Pitfalls

Implementation

• Rate Limiting - Exceeding API quotas and request limits
• Cost Management - Unexpected expenses from high token usage
• Error Handling - Inadequate handling of API failures
• Version Control - Issues with model version compatibility

Performance

• Latency Issues - Slow response times affecting user experience
• Resource Usage - High computational requirements
• Scalability Problems - Difficulties handling increased load
• Memory Management - Issues with token context windows

Best Practices

Input Design

• Use clear, specific instructions
• Provide sufficient context
• Implement input validation
• Test with diverse prompts

Output Handling

• Validate response accuracy
• Implement content filtering
• Monitor response quality
• Handle errors gracefully

Technical Implementation

• Use retry mechanisms
• Implement rate limiting
• Monitor costs actively
• Version control prompts

Safety Measures

• Content moderation
• Data privacy controls
• Bias detection
• Regular auditing

Additional Resources

Documentation

• OpenAI Safety Best Practices
• Google AI Safety Guide
• Anthropic AI Safety

Research Papers

• Language Models: Risks & Limitations
• Challenges in Deploying LLMs
• Adversarial Attacks on LLMs
• Factuality in Language Models

Tools and Frameworks

• LangChain Safety Tools
• Guardrails AI
• Guardrails Guide
• TruLens Evaluations
• ProtectAI - LLM security and safety platform