Language Learning Models (LLMs) are rapidly evolving, becoming more sophisticated and versatile. To harness their full potential, crafting effective prompts is essential for optimal functionality.
Crafting the best prompts is not an exact science. Different approaches can yield different results depending on the model and its training. Continuous training and fine-tuning are crucial for achieving the best outcomes. However, fine-tuning data isn’t always available.
Common Failure Cases
One major failure occurs when too many instructions are packed into a single prompt, leading to confusion and suboptimal responses.
Solution: Multiple Agents with Different Angles
One approach to mitigate this issue is employing multiple agents to examine the conversation from various perspectives. However, this introduces a new challenge. Most LLMs are designed to handle ongoing, consistent conversations. If an agent is given a conversation from another agent, it may interpret the previous messages as examples of how to respond, rather than the actual conversation history.
Effective Solutions
1. Summarize the Conversation
Before engaging a new agent, summarize the existing conversation. Provide the summary as input in a new conversation thread. This method not only clarifies context but also reduces token usage.
2. Context Switch in System Message
Incorporate the context switch directly into the system message. This strategy ensures that the conversation history aligns with the new system prompt, preventing conflict between historical context and the current prompt.
Example:
Then, when prompting the agent,
Conclusion
Mastering the art of prompting LLM agents involves clarity, precision, and strategic use of multiple agents. By summarizing conversations and integrating context switches into system messages, you can enhance the performance and accuracy of LLM responses.