Gemini vs Claude Context Window SECRETS for AI Orchestration
AI Summary
This video discusses critical differences between Gemini and Claude AI models when building multi-agent orchestrations, focusing on their context window limitations and how to prompt them effectively.
Key Points:
Context Window Differences:
- Gemini: 1 million context window - can effectively “read documents” and maintain contextual understanding
- Claude: 200k context window - more limited and requires different prompting strategies
Gemini Prompting Strategy:
- Can tell Gemini to “look at these three documents” for complete contextual understanding
- Works well with external document references due to large context window
- Can handle complex multi-document tasks effectively
Claude Prompting Challenges:
- The Problem: When told to reference external files, Claude reads them but then forgets the initial context as it processes more files
- Result: Claude enters loops and fails to maintain coherent understanding
- Root Cause: 200k context window limitation prevents effective memory of past context
Correct Claude Prompting Strategy:
- Embed everything in the prompt: All necessary information must be included directly in the prompt itself
- Avoid external references: Don’t guide Claude to other documents or files for information
- Self-contained prompts: Each prompt should contain all context needed for the task
- No past memory reliance: Claude doesn’t effectively remember previous context due to window limitations
Best Practices for Multi-Agent Orchestrations:
- Always consider the specific model’s context window limitations at every point in your orchestration
- Tailor prompting strategies to each model’s strengths and limitations
- Bad prompting often stems from not accounting for model-specific constraints
- Design your AI agent flows with model capabilities in mind
Common Mistakes:
- Using the same prompting strategy for different models
- Not considering context window limitations when designing agent workflows
- Expecting Claude to maintain long-term context like models with larger windows