LLM Limitations and Boundaries

Fundamental Limitations

Knowledge Cutoff LLMs have training data cutoff dates:

Implication: Cannot answer about recent events without retrieval.

Context Window Constraints

• Maximum tokens per request (e.g., 128K, 200K)
• "Lost in the middle" problem for very long contexts
• Cost scales with context length

Hallucinations LLMs may generate:

• Plausible-sounding but false information
• Non-existent citations or references
• Confident answers about things they do not know

What LLMs Cannot Do Well

Reliable Computation

Real-time Information

• No access to current events
• Cannot check live stock prices, weather
• Solution: Tool use, RAG with current data

Precision Tasks

Guaranteed Safety

• Jailbreaks and prompt injection possible
• Cannot guarantee 100% filter compliance
• Requires defense in depth approach

Things to Be Careful About

High-Stakes Decisions ❌ LLMs should not be sole deciders for:

• Medical diagnoses
• Legal advice
• Financial decisions
• Safety-critical systems

✅ Use as assistants with human oversight

Private Information

• LLMs may memorize training data
• API calls may be logged by providers
• Consider privacy implications

Consistency

• Same prompt may give different outputs
• Temperature=0 helps but not guaranteed
• For critical consistency, verify programmatically

Mitigation Strategies

For Hallucinations 1. Use RAG with verified sources 2. Request citations and verify them 3. Cross-check with multiple queries 4. Add fact-checking step

For Math/Logic 1. Use code execution tools 2. Chain-of-thought prompting 3. Self-consistency (multiple samples) 4. Formal verification where possible

For Safety 1. Layer multiple guardrails 2. Content filtering on input/output 3. Human review for sensitive content 4. Rate limiting and monitoring